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Liu Y, Hong X, Tao X, Dong S, Shi J, Gong Y. Model Behavior Preserving for Class-Incremental Learning. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:7529-7540. [PMID: 35120008 DOI: 10.1109/tnnls.2022.3144183] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Deep models have shown to be vulnerable to catastrophic forgetting, a phenomenon that the recognition performance on old data degrades when a pre-trained model is fine-tuned on new data. Knowledge distillation (KD) is a popular incremental approach to alleviate catastrophic forgetting. However, it usually fixes the absolute values of neural responses for isolated historical instances, without considering the intrinsic structure of the responses by a convolutional neural network (CNN) model. To overcome this limitation, we recognize the importance of the global property of the whole instance set and treat it as a behavior characteristic of a CNN model relevant to model incremental learning. On this basis: 1) we design an instance neighborhood-preserving (INP) loss to maintain the order of pair-wise instance similarities of the old model in the feature space; 2) we devise a label priority-preserving (LPP) loss to preserve the label ranking lists within instance-wise label probability vectors in the output space; and 3) we introduce an efficient derivable ranking algorithm for calculating the two loss functions. Extensive experiments conducted on CIFAR100 and ImageNet show that our approach achieves the state-of-the-art performance.
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Liu L, Kuang Z, Chen Y, Xue JH, Yang W, Zhang W. IncDet: In Defense of Elastic Weight Consolidation for Incremental Object Detection. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:2306-2319. [PMID: 32598286 DOI: 10.1109/tnnls.2020.3002583] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Elastic weight consolidation (EWC) has been successfully applied for general incremental learning to overcome the catastrophic forgetting issue. It adaptively constrains each parameter of the new model not to deviate much from its counterpart in the old model during fine-tuning on new class data sets, according to its importance weight for old tasks. However, the previous study demonstrates that it still suffers from catastrophic forgetting when directly used in object detection. In this article, we show EWC is effective for incremental object detection if with critical adaptations. First, we conduct controlled experiments to identify two core issues why EWC fails if trivially applied to incremental detection: 1) the absence of old class annotations in new class images makes EWC misclassify objects of old classes in these images as background and 2) the quadratic regularization loss in EWC easily leads to gradient explosion when balancing old and new classes. Then, based on the abovementioned findings, we propose the corresponding solutions to tackle these issues: 1) utilize pseudobounding box annotations of old classes on new data sets to compensate for the absence of old class annotations and 2) adopt a novel Huber regularization instead of the original quadratic loss to prevent from unstable training. Finally, we propose a general EWC-based incremental object detection framework and implement it under both Fast R-CNN and Faster R-CNN, showing its flexibility and versatility. In terms of either the final performance or the performance drop with respect to the upper bound of joint training on all seen classes, evaluations on the PASCAL VOC and COCO data sets show that our method achieves a new state of the art.
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Ororbia A, Mali A, Giles CL, Kifer D. Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:4267-4278. [PMID: 31976910 DOI: 10.1109/tnnls.2019.2953622] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Temporal models based on recurrent neural networks have proven to be quite powerful in a wide variety of applications, including language modeling and speech processing. However, training these models often relies on backpropagation through time (BPTT), which entails unfolding the network over many time steps, making the process of conducting credit assignment considerably more challenging. Furthermore, the nature of backpropagation itself does not permit the use of nondifferentiable activation functions and is inherently sequential, making parallelization of the underlying training process difficult. Here, we propose the parallel temporal neural coding network (P-TNCN), a biologically inspired model trained by the learning algorithm we call local representation alignment. It aims to resolve the difficulties and problems that plague recurrent networks trained by BPTT. The architecture requires neither unrolling in time nor the derivatives of its internal activation functions. We compare our model and learning procedure with other BPTT alternatives (which also tend to be computationally expensive), including real-time recurrent learning, echo state networks, and unbiased online recurrent optimization. We show that it outperforms these on-sequence modeling benchmarks such as Bouncing MNIST, a new benchmark we denote as Bouncing NotMNIST, and Penn Treebank. Notably, our approach can, in some instances, outperform full BPTT as well as variants such as sparse attentive backtracking. Significantly, the hidden unit correction phase of P-TNCN allows it to adapt to new data sets even if its synaptic weights are held fixed (zero-shot adaptation) and facilitates retention of prior generative knowledge when faced with a task sequence. We present results that show the P-TNCN's ability to conduct zero-shot adaptation and online continual sequence modeling.
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Employing Image Processing Techniques and Artificial Intelligence for Automated Eye Diagnosis Using Digital Eye Fundus Images. ACTA ACUST UNITED AC 2018. [DOI: 10.4028/www.scientific.net/jbbbe.39.40] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Blindness usually comes from two main causes, glaucoma and diabetes. Robust mass screening is performed for diagnosing, such as screening that requires a cost-effective method for glaucoma and diabetic retinopathy and integrates well with digital medical imaging, image processing, and administrative processes. For addressing all these issues, we propose a novel low-cost automated glaucoma and diabetic retinopathy diagnosis system, based on features extraction from digital eye fundus images. This paper proposes a diagnosis system for automated identification of healthy, glaucoma, and diabetic retinopathy. Using a combination of local binary pattern features, Gabor filter features, statistical features, and color features which are then fed to an artificial neural network and support vector machine classifiers. In this work, the classifier identifies healthy, glaucoma, and diabetic retinopathy images with an accuracy of 91.1%,92.9%, 92.9%, and 92.3% and sensitivity of 91.06%, 92.6%, 92.66%, and 91.73% and specificity of 89.83%, 91.26%, 91.96%, and 89.16% for ANN, and an accuracy of 90.0%,92.94%, 95.43%, and 97.92% and sensitivity of 89.34%, 93.26%, 95.72%, and 97.93% and specificity of 95.13%, 96.68%, 97.88%, and 99.05% for SVM, based on 5, 10, 15, and 31 number of selected features. The proposed system can detect glaucoma, diabetic retinopathy and normal cases with high accuracy and sensitivity using selected features, the performance of the system is high due to using of a huge fundus database.
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Ranjan NM, Prasad RS. Automatic text classification using BPLion-neural network and semantic word processing. THE IMAGING SCIENCE JOURNAL 2017. [DOI: 10.1080/13682199.2017.1376781] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Misir R, Mitra M, Samanta RK. A Reduced Set of Features for Chronic Kidney Disease Prediction. J Pathol Inform 2017; 8:24. [PMID: 28706750 PMCID: PMC5497482 DOI: 10.4103/jpi.jpi_88_16] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2016] [Accepted: 02/23/2017] [Indexed: 01/07/2023] Open
Abstract
Chronic kidney disease (CKD) is one of the life-threatening diseases. Early detection and proper management are solicited for augmenting survivability. As per the UCI data set, there are 24 attributes for predicting CKD or non-CKD. At least there are 16 attributes need pathological investigations involving more resources, money, time, and uncertainties. The objective of this work is to explore whether we can predict CKD or non-CKD with reasonable accuracy using less number of features. An intelligent system development approach has been used in this study. We attempted one important feature selection technique to discover reduced features that explain the data set much better. Two intelligent binary classification techniques have been adopted for the validity of the reduced feature set. Performances were evaluated in terms of four important classification evaluation parameters. As suggested from our results, we may more concentrate on those reduced features for identifying CKD and thereby reduces uncertainty, saves time, and reduces costs.
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Affiliation(s)
- Rajesh Misir
- Department of Computer Science, Vidyasagar University, Medinipur, India
| | - Malay Mitra
- Department of Computer Science and Application, Expert Systems Laboratory, University of North Bengal, Darjeeling, West Bengal, India
| | - Ranjit Kumar Samanta
- Department of Computer Science and Application, Expert Systems Laboratory, University of North Bengal, Darjeeling, West Bengal, India
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Abdollahi Y, Sairi NA, Said SBM, Abouzari-lotf E, Zakaria A, Sabri MFBM, Islam A, Alias Y. Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2015; 150:892-901. [PMID: 26119355 DOI: 10.1016/j.saa.2015.06.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2014] [Revised: 05/26/2015] [Accepted: 06/12/2015] [Indexed: 06/04/2023]
Abstract
It is believe that 80% industrial of carbon dioxide can be controlled by separation and storage technologies which use the blended ionic liquids absorber. Among the blended absorbers, the mixture of water, N-methyldiethanolamine (MDEA) and guanidinium trifluoromethane sulfonate (gua) has presented the superior stripping qualities. However, the blended solution has illustrated high viscosity that affects the cost of separation process. In this work, the blended fabrication was scheduled with is the process arranging, controlling and optimizing. Therefore, the blend's components and operating temperature were modeled and optimized as input effective variables to minimize its viscosity as the final output by using back-propagation artificial neural network (ANN). The modeling was carried out by four mathematical algorithms with individual experimental design to obtain the optimum topology using root mean squared error (RMSE), R-squared (R(2)) and absolute average deviation (AAD). As a result, the final model (QP-4-8-1) with minimum RMSE and AAD as well as the highest R(2) was selected to navigate the fabrication of the blended solution. Therefore, the model was applied to obtain the optimum initial level of the input variables which were included temperature 303-323 K, x[gua], 0-0.033, x[MDAE], 0.3-0.4, and x[H2O], 0.7-1.0. Moreover, the model has obtained the relative importance ordered of the variables which included x[gua]>temperature>x[MDEA]>x[H2O]. Therefore, none of the variables was negligible in the fabrication. Furthermore, the model predicted the optimum points of the variables to minimize the viscosity which was validated by further experiments. The validated results confirmed the model schedulability. Accordingly, ANN succeeds to model the initial components of the blended solutions as absorber of CO2 capture in separation technologies that is able to industries scale up.
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Affiliation(s)
- Yadollah Abdollahi
- Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Nor Asrina Sairi
- Department of Chemistry, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia.
| | - Suhana Binti Mohd Said
- Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Ebrahim Abouzari-lotf
- Institute of Hydrogen Economy, Energy Research Alliance, International Campus, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia
| | - Azmi Zakaria
- Material Synthesis and Characterization Laboratory, Institute of Advanced Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
| | | | - Aminul Islam
- Catalysis and Science Research Center, Faculty of Science, University Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
| | - Yatimah Alias
- Department of Chemistry, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia
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Mathur A, Tripathi AS, Kuse M. Scalable system for classification of white blood cells from Leishman stained blood stain images. J Pathol Inform 2013; 4:S15. [PMID: 23766937 PMCID: PMC3678750 DOI: 10.4103/2153-3539.109883] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2013] [Accepted: 01/21/2013] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION The White Blood Cell (WBC) differential count yields clinically relevant information about health and disease. Currently, pathologists manually annotate the WBCs, which is time consuming and susceptible to error, due to the tedious nature of the process. This study aims at automation of the Differential Blood Count (DBC) process, so as to increase productivity and eliminate human errors. MATERIALS AND METHODS The proposed system takes the peripheral Leishman blood stain images as the input and generates a count for each of the WBC subtypes. The digitized microscopic images are stain normalized for the segmentation, to be consistent over a diverse set of slide images. Active contours are employed for robust segmentation of the WBC nucleus and cytoplasm. The seed points are generated by processing the images in Hue-Saturation-Value (HSV) color space. An efficient method for computing a new feature, 'number of lobes,' for discrimination of WBC subtypes, is introduced in this article. This method is based on the concept of minimization of the compactness of each lobe. The Naive Bayes classifier, with Laplacian correction, provides a fast, efficient, and robust solution to multiclass categorization problems. This classifier is characterized by incremental learning and can also be embedded within the database systems. RESULTS An overall accuracy of 92.45% and 92.72% over the training and testing sets has been obtained, respectively. CONCLUSION Thus, incremental learning is inducted into the Naive Bayes Classifier, to facilitate fast, robust, and efficient classification, which is evident from the high sensitivity achieved for all the subtypes of WBCs.
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Affiliation(s)
- Atin Mathur
- Department of Computer Science, The LNM Institute of Information Technology, Jaipur, India
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Prediction model of ammonium uranyl carbonate calcination by microwave heating using incremental improved Back-Propagation neural network. NUCLEAR ENGINEERING AND DESIGN 2011. [DOI: 10.1016/j.nucengdes.2010.12.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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HCBPM: An Idea toward a Social Learning Environment for Humanoid Robot. JOURNAL OF ROBOTICS 2010. [DOI: 10.1155/2010/241785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
To advance robotics toward real-world applications, a growing body of research has focused on the development of control systems for humanoid robots in recent years. Several approaches have been proposed to support the learning stage of such controllers, where the robot can learn new behaviors by observing and/or receiving direct guidance from a human or even another robot. These approaches require dynamic learning and memorization techniques, which the robot can use to reform and update its internal systems continuously while learning new behaviors. Against this background, this study investigates a new approach to the development of an incremental learning and memorization model. This approach was inspired by the principles of neuroscience, and the developed model was named “Hierarchical Constructive Backpropagation with Memory” (HCBPM). The validity of the model was tested by teaching a humanoid robot to recognize a group of objects through natural interaction. The experimental results indicate that the proposed model efficiently enhances real-time machine learning in general and can be used to establish an environment suitable for social learning between the robot and the user in particular.
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Sun J. Local coupled feedforward neural network. Neural Netw 2009; 23:108-13. [PMID: 19596550 DOI: 10.1016/j.neunet.2009.06.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2008] [Revised: 06/22/2009] [Accepted: 06/24/2009] [Indexed: 11/18/2022]
Abstract
In this paper, the local coupled feedforward neural network is presented. Its connection structure is same as that of Multilayer Perceptron with one hidden layer. In the local coupled feedforward neural network, each hidden node is assigned an address in an input space, and each input activates only the hidden nodes near it. For each input, only the activated hidden nodes take part in forward and backward propagation processes. Theoretical analysis and simulation results show that this neural network owns the "universal approximation" property and can solve the learning problem of feedforward neural networks. In addition, its characteristic of local coupling makes knowledge accumulation possible.
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Affiliation(s)
- Jianye Sun
- Computation Center, Harbin University of Science and Technology, No. 52, Xuefu Road, Harbin, PR China.
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Image quality in image classification: Adaptive image quality modification with adaptive classification. Comput Chem Eng 2009. [DOI: 10.1016/j.compchemeng.2008.10.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Park MS, Choi JY. Evolving logic networks with real-valued inputs for fast incremental learning. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS. PART B, CYBERNETICS : A PUBLICATION OF THE IEEE SYSTEMS, MAN, AND CYBERNETICS SOCIETY 2009; 39:254-267. [PMID: 19068435 DOI: 10.1109/tsmcb.2008.2005483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
In this paper, we present a neural network structure and a fast incremental learning algorithm using this network. The proposed network structure, named Evolving Logic Networks for Real-valued inputs (ELN-R), is a data structure for storing and using the knowledge. A distinctive feature of ELN-R is that the previously learned knowledge stored in ELN-R can be used as a kind of building block in constructing new knowledge. Using this feature, the proposed learning algorithm can enhance the stability and plasticity at the same time, and as a result, the fast incremental learning can be realized. The performance of the proposed scheme is shown by a theoretical analysis and an experimental study on two benchmark problems.
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Affiliation(s)
- Myoung Soo Park
- School of Electrical Engineering and Computer Science, Automation and Systems Research Institute, Seoul National University, Seoul 151-744, Korea
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Muhlbaier M, Topalis A, Polikar R. Learn$^{++}$.NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes. ACTA ACUST UNITED AC 2009; 20:152-68. [DOI: 10.1109/tnn.2008.2008326] [Citation(s) in RCA: 139] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Weaver S, Baird L, Polycarpou M. Using localizing learning to improve supervised learning algorithms. ACTA ACUST UNITED AC 2008; 12:1037-46. [PMID: 18249931 DOI: 10.1109/72.950133] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Slow learning of neural-network function approximators can frequently be attributed to interference, which occurs when learning in one area of the input space causes unlearning in another area. To mitigate the effect of unlearning, this paper develops an algorithm that adjusts the weights of an arbitrary, nonlinearly parameterized network such that the potential for future interference during learning is reduced. This is accomplished by the reduction of a biobjective cost function that combines the approximation error and a term that measures interference. An analysis of the algorithm's convergence properties shows that learning with this algorithm reduces future unlearning. The algorithm can be used either during online learning or can be used to condition a network to have immunity from interference during a future learning stage. A simple example demonstrates how interference manifests itself in a network and how less interference can lead to more efficient learning. Simulations demonstrate how this new learning algorithm speeds up the training in various situations due to the extra cost function term.
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Affiliation(s)
- S Weaver
- GenomatixUSA, Cincinnati, OH 45221, USA.
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Murphey YL, Chen ZH, Feldkamp LA. An incremental neural learning framework and its application to vehicle diagnostics. APPL INTELL 2007. [DOI: 10.1007/s10489-007-0040-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Yamauchi K, Oohira T, Omori T. Fast incremental learning methods inspired by biological learning behavior. ARTIFICIAL LIFE AND ROBOTICS 2005. [DOI: 10.1007/s10015-004-0325-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Abstract
Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multiagent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an "integration" operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed.
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Affiliation(s)
- Sheng-Uei Guan
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 119260.
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Polikar R, Udpa L, Udpa S, Honavar V. An incremental learning algorithm with confidence estimation for automated identification of NDE signals. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2004; 51:990-1001. [PMID: 15344404 DOI: 10.1109/tuffc.2004.1324403] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
An incremental learning algorithm is introduced for learning new information from additional data that may later become available, after a classifier has already been trained using a previously available database. The proposed algorithm is capable of incrementally learning new information without forgetting previously acquired knowledge and without requiring access to the original database, even when new data include examples of previously unseen classes. Scenarios requiring such a learning algorithm are encountered often in nondestructive evaluation (NDE) in which large volumes of data are collected in batches over a period of time, and new defect types may become available in subsequent databases. The algorithm, named Learn++, takes advantage of synergistic generalization performance of an ensemble of classifiers in which each classifier is trained with a strategically chosen subset of the training databases that subsequently become available. The ensemble of classifiers then is combined through a weighted majority voting procedure. Learn++ is independent of the specific classifier(s) comprising the ensemble, and hence may be used with any supervised learning algorithm. The voting procedure also allows Learn++ to estimate the confidence in its own decision. We present the algorithm and its promising results on two separate ultrasonic weld inspection applications.
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Affiliation(s)
- Robi Polikar
- Department of Electrical and Computer Engineering, Rowan University, Glassboro, NJ 08028, USA.
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Superimposing neural learning by dynamic and spatial changing weights. ARTIFICIAL LIFE AND ROBOTICS 2004. [DOI: 10.1007/bf02471197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Meesad P, Yen G. Combined numerical and linguistic knowledge representation and its application to medical diagnosis. ACTA ACUST UNITED AC 2003. [DOI: 10.1109/tsmca.2003.811290] [Citation(s) in RCA: 51] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Chakraborty D, Pal N. A novel training scheme for multilayered perceptrons to realize proper generalization and incremental learning. ACTA ACUST UNITED AC 2003; 14:1-14. [DOI: 10.1109/tnn.2002.806953] [Citation(s) in RCA: 38] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Polikar R, Upda L, Upda S, Honavar V. Learn++: an incremental learning algorithm for supervised neural networks. ACTA ACUST UNITED AC 2001. [DOI: 10.1109/5326.983933] [Citation(s) in RCA: 549] [Impact Index Per Article: 23.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Hoya T, Chambers J. Heuristic pattern correction scheme using adaptively trained generalized regression neural networks. ACTA ACUST UNITED AC 2001; 12:91-100. [DOI: 10.1109/72.896798] [Citation(s) in RCA: 20] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Efe MO, Kaynak O. Stabilizing and robustifying the learning mechanisms of artificial neural networks in control engineering applications. INT J INTELL SYST 2000. [DOI: 10.1002/(sici)1098-111x(200005)15:5<365::aid-int1>3.0.co;2-p] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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An incremental-learning neural network for the classification of remote-sensing images. Pattern Recognit Lett 1999. [DOI: 10.1016/s0167-8655(99)00091-4] [Citation(s) in RCA: 38] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Yamauchi K, Yamaguchi N, Ishii N. Incremental learning methods with retrieving of interfered patterns. ACTA ACUST UNITED AC 1999; 10:1351-65. [DOI: 10.1109/72.809080] [Citation(s) in RCA: 74] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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