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Xin J, Khishe M, Zeebaree DQ, Abualigah L, Ghazal TM. Adaptive habitat biogeography-based optimizer for optimizing deep CNN hyperparameters in image classification. Heliyon 2024; 10:e28147. [PMID: 38689992 PMCID: PMC11059399 DOI: 10.1016/j.heliyon.2024.e28147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 03/12/2024] [Accepted: 03/12/2024] [Indexed: 05/02/2024] Open
Abstract
Deep Convolutional Neural Networks (DCNNs) have shown remarkable success in image classification tasks, but optimizing their hyperparameters can be challenging due to their complex structure. This paper develops the Adaptive Habitat Biogeography-Based Optimizer (AHBBO) for tuning the hyperparameters of DCNNs in image classification tasks. In complicated optimization problems, the BBO suffers from premature convergence and insufficient exploration. In this regard, an adaptable habitat is presented as a solution to these problems; it would permit variable habitat sizes and regulated mutation. Better optimization performance and a greater chance of finding high-quality solutions across a wide range of problem domains are the results of this modification's increased exploration and population diversity. AHBBO is tested on 53 benchmark optimization functions and demonstrates its effectiveness in improving initial stochastic solutions and converging faster to the optimum. Furthermore, DCNN-AHBBO is compared to 23 well-known image classifiers on nine challenging image classification problems and shows superior performance in reducing the error rate by up to 5.14%. Our proposed algorithm outperforms 13 benchmark classifiers in 87 out of 95 evaluations, providing a high-performance and reliable solution for optimizing DNNs in image classification tasks. This research contributes to the field of deep learning by proposing a new optimization algorithm that can improve the efficiency of deep neural networks in image classification.
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Affiliation(s)
- Jiayun Xin
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, 264209, Shandong, China
| | - Mohammad Khishe
- Department of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
- Center for Artificial Intelligence Applications, Yuan Ze University, Taiwan
| | - Diyar Qader Zeebaree
- Information Technology Department, Technical College of Duhok, Duhok Polytechnic University, Duhok, Iraq
| | - Laith Abualigah
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan
- Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan
- Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia
- MEU Research Unit, Middle East University, Amman, 11831, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
- School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya, 27500, Malaysia
| | - Taher M. Ghazal
- Centre for Cyber Physical Systems, Computer Science Department, Khalifa University, United Arab Emirates
- Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Selangor, Malaysia
- Applied Science Research Center, Applied Science Private University, Amman, 11937, Jordan
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Rahimi MR, Makarem D, Sarspy S, Mahdavi SA, Albaghdadi MF, Armaghan SM. Classification of cancer cells and gene selection based on microarray data using MOPSO algorithm. J Cancer Res Clin Oncol 2023; 149:15171-15184. [PMID: 37634207 DOI: 10.1007/s00432-023-05308-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 08/16/2023] [Indexed: 08/29/2023]
Abstract
PURPOSE Microarray information is crucial for the identification and categorisation of malignant tissues. The very limited sample size in the microarray has always been a challenge for classification design in cancer research. As a result, by pre-processing gene selection approaches and genes lacking their information, the microarray data are deleted prior to categorisation. In essence, an appropriate gene selection technique can significantly increase the accuracy of illness (cancer) classification. METHODS For the classification of high-dimensional microarray data, a novel approach based on the hybrid model of multi-objective particle swarm optimisation (MOPSO) is proposed in this research. First, a binary vector representing each particle's position is presented at random. A gene is represented by each bit. Bit 0 denotes the absence of selection of the characteristic (gene) corresponding to it, while bit 1 denotes the selection of the gene. Therefore, the position of each particle represents a set of genes, and the linear Bayesian discriminant analysis classification algorithm calculates each particle's degree of fitness to assess the quality of the gene set that particle has chosen. The suggested methodology is applied to four different cancer database sets, and the results are contrasted with those of other approaches currently in use. RESULTS The proposed algorithm has been applied on four sets of cancer database and its results have been compared with other existing methods. The results of the implementation show that the improvement of classification accuracy in the proposed algorithm compared to other methods for four sets of databases is 25.84% on average. So that it has improved by 18.63% in the blood cancer database, 24.25% in the lung cancer database, 27.73% in the breast cancer database, and 32.80% in the prostate cancer database. Therefore, the proposed algorithm is able to identify a small set of genes containing information in a way choose to increase the classification accuracy. CONCLUSION Our proposed solution is used for data classification, which also improves classification accuracy. This is possible because the MOPSO model removes redundancy and reduces the number of redundant and redundant genes by considering how genes are correlated with each other.
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Affiliation(s)
| | - Dorna Makarem
- Escuela Tecnica Superior de Ingenieros de Telecomunicacion Politecnica de Madrid, Madrid, Spain
| | - Sliva Sarspy
- Department of Computer Science, College of Science, Cihan University-Erbil, Erbil, Iraq
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Qiu F, Zheng P, Heidari AA, Liang G, Chen H, Karim FK, Elmannai H, Lin H. Mutational Slime Mould Algorithm for Gene Selection. Biomedicines 2022; 10:biomedicines10082052. [PMID: 36009599 PMCID: PMC9406076 DOI: 10.3390/biomedicines10082052] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/14/2022] [Accepted: 08/16/2022] [Indexed: 02/02/2023] Open
Abstract
A large volume of high-dimensional genetic data has been produced in modern medicine and biology fields. Data-driven decision-making is particularly crucial to clinical practice and relevant procedures. However, high-dimensional data in these fields increase the processing complexity and scale. Identifying representative genes and reducing the data’s dimensions is often challenging. The purpose of gene selection is to eliminate irrelevant or redundant features to reduce the computational cost and improve classification accuracy. The wrapper gene selection model is based on a feature set, which can reduce the number of features and improve classification accuracy. This paper proposes a wrapper gene selection method based on the slime mould algorithm (SMA) to solve this problem. SMA is a new algorithm with a lot of application space in the feature selection field. This paper improves the original SMA by combining the Cauchy mutation mechanism with the crossover mutation strategy based on differential evolution (DE). Then, the transfer function converts the continuous optimizer into a binary version to solve the gene selection problem. Firstly, the continuous version of the method, ISMA, is tested on 33 classical continuous optimization problems. Then, the effect of the discrete version, or BISMA, was thoroughly studied by comparing it with other gene selection methods on 14 gene expression datasets. Experimental results show that the continuous version of the algorithm achieves an optimal balance between local exploitation and global search capabilities, and the discrete version of the algorithm has the highest accuracy when selecting the least number of genes.
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Affiliation(s)
- Feng Qiu
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Pan Zheng
- Information Systems, University of Canterbury, Christchurch 8014, New Zealand
| | - Ali Asghar Heidari
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
| | - Guoxi Liang
- Department of Information Technology, Wenzhou Polytechnic, Wenzhou 325035, China
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
- Correspondence:
| | - Faten Khalid Karim
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Hela Elmannai
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Haiping Lin
- Department of Information Engineering, Hangzhou Vocational & Technical College, Hangzhou 310018, China
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Dokeroglu T, Deniz A, Kiziloz HE. A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.083] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Na X, Han M, Ren W, Zhong K. Modified BBO-Based Multivariate Time-Series Prediction System With Feature Subset Selection and Model Parameter Optimization. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:2163-2173. [PMID: 32639932 DOI: 10.1109/tcyb.2020.2977375] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Multivariate time-series prediction is a challenging research topic in the field of time-series analysis and modeling, and is continually under research. The echo state network (ESN), a type of efficient recurrent neural network, has been widely used in time-series prediction, but when using ESN, two crucial problems have to be confronted: 1) how to select the optimal subset of input features and 2) how to set the suitable parameters of the model. To solve this problem, the modified biogeography-based optimization ESN (MBBO-ESN) system is proposed for system modeling and multivariate time-series prediction, which can simultaneously achieve feature subset selection and model parameter optimization. The proposed MBBO algorithm is an improved evolutionary algorithm based on biogeography-based optimization (BBO), which utilizes an S -type population migration rate model, a covariance matrix migration strategy, and a Lévy distribution mutation strategy to enhance the rotation invariance and exploration ability. Furthermore, the MBBO algorithm cannot only optimize the key parameters of the ESN model but also uses a hybrid-metric feature selection method to remove the redundancies and distinguish the importance of the input features. Compared with the traditional methods, the proposed MBBO-ESN system can discover the relationship between the input features and the model parameters automatically and make the prediction more accurate. The experimental results on the benchmark and real-world datasets demonstrate that MBBO outperforms the other traditional evolutionary algorithms, and the MBBO-ESN system is more competitive in multivariate time-series prediction than other classic machine-learning models.
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Ma S, Wang Y, Zhang S. Modified chemical reaction optimization and its application in engineering problems. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:7143-7160. [PMID: 34814243 DOI: 10.3934/mbe.2021354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Chemical Reaction Optimization (CRO) is a simple and efficient evolutionary optimization algorithm by simulating chemical reactions. As far as the current research is concerned, the algorithm has been successfully used for solving a number of real-world optimization tasks. In our paper, a new real encoded chemical reaction optimization algorithm is proposed to boost the efficiency of the optimization operations in standard chemical reactions optimization algorithm. Inspired by the evolutionary operation of the differential evolution algorithm, an improved search operation mechanism is proposed based on the underlying operation. It is modeled to further explore the search space of the algorithm under the best individuals. Afterwards, to control the perturbation frequency of the search strategy, the modification rate is increased to balance between the exploration ability and mining ability of the algorithm. Meanwhile, we also propose a new population initialization method that incorporates several models to produce high-quality initialized populations. To validate the effectiveness of the algorithm, nine unconstrained optimization algorithms are used as benchmark functions. As observed from the experimental results, it is evident that the proposed algorithm is significantly better than the standard chemical reaction algorithm and other evolutionary optimization algorithms. Then, we also apply the proposed model to address the synthesis problem of two antenna array synthesis. The results also reveal that the proposed algorithm is superior to other approaches from different perspectives.
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Affiliation(s)
- Shijing Ma
- School of Physical Education, Northeast Normal University, Changchun 130022, China
| | - Yunhe Wang
- School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
| | - Shouwei Zhang
- School of Physical Education, Northeast Normal University, Changchun 130022, China
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Yao Y, Zhao X, Ning Q, Zhou J. ABC-Gly: Identifying Protein Lysine Glycation Sites with Artificial Bee Colony Algorithm. CURR PROTEOMICS 2021. [DOI: 10.2174/1570164617666191227120136] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Glycation is a nonenzymatic post-translational modification process by attaching
a sugar molecule to a protein or lipid molecule. It may impair the function and change the characteristic
of the proteins which may lead to some metabolic diseases. In order to understand the underlying molecular
mechanisms of glycation, computational prediction methods have been developed because of their
convenience and high speed. However, a more effective computational tool is still a challenging task in
computational biology.
Methods:
In this study, we showed an accurate identification tool named ABC-Gly for predicting lysine
glycation sites. At first, we utilized three informative features, including position-specific amino
acid propensity, secondary structure and the composition of k-spaced amino acid pairs to encode the
peptides. Moreover, to sufficiently exploit discriminative features thus can improve the prediction and
generalization ability of the model, we developed a two-step feature selection, which combined the
Fisher score and an improved binary artificial bee colony algorithm based on the support vector machine.
Finally, based on the optimal feature subset, we constructed an effective model by using the
Support Vector Machine on the training dataset.
Results:
The performance of the proposed predictor ABC-Gly was measured with the sensitivity of
76.43%, the specificity of 91.10%, the balanced accuracy of 83.76%, the Area Under the receiveroperating
characteristic Curve (AUC) of 0.9313, a Matthew’s Correlation Coefficient (MCC) of
0.6861 by 10-fold cross-validation on training dataset, and a balanced accuracy of 59.05% on independent
dataset. Compared to the state-of-the-art predictors on the training dataset, the proposed predictor
achieved significant improvement in the AUC of 0.156 and MCC of 0.336.
Conclusion:
The detailed analysis results indicated that our predictor may serve as a powerful complementary
tool to other existing methods for predicting protein lysine glycation. The source code and
datasets of the ABC-Gly were provided in the Supplementary File 1.
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Affiliation(s)
- Yanqiu Yao
- College of Computer Science and Technology, Changchun Normal University, Changchun, 130032, China
| | - Xiaosa Zhao
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China
| | - Qiao Ning
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China
| | - Junping Zhou
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China
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Multi-variant differential evolution algorithm for feature selection. Sci Rep 2020; 10:17261. [PMID: 33057120 PMCID: PMC7560894 DOI: 10.1038/s41598-020-74228-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 09/28/2020] [Indexed: 11/29/2022] Open
Abstract
This work introduces a new population-based stochastic search technique, named multi-variant differential evolution (MVDE) algorithm for solving fifteen well-known real world problems from UCI repository and compared to four popular optimization methods. The MVDE proposes a new self-adaptive scaling factor based on cosine and logistic distributions as an almost factor-free optimization technique. For more updated chances, this factor is binary-mapped by incorporating an adaptive crossover operator. During the evolution, both greedy and less-greedy variants are managed by adjusting and incorporating the binary scaling factor and elite identification mechanism into a new multi-mutation crossover process through a number of sequentially evolutionary phases. Feature selection decreases the number of features by eliminating irrelevant or misleading, noisy and redundant data which can accelerate the process of classification. In this paper, a new feature selection algorithm based on the MVDE method and artificial neural network is presented which enabled MVDE to get a combination features’ set, accelerate the accuracy of the classification, and optimize both the structure and weights of Artificial Neural Network (ANN) simultaneously. The experimental results show the encouraging behavior of the proposed algorithm in terms of the classification accuracies and optimal number of feature selection.
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Ramya J, Somasundareswari D, Vijayalakshmi P. Gas chimney and hydrocarbon detection using combined BBO and artificial neural network with hybrid seismic attributes. Soft comput 2019. [DOI: 10.1007/s00500-019-04064-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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10
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Classification of high dimensional biomedical data based on feature selection using redundant removal. PLoS One 2019; 14:e0214406. [PMID: 30964868 PMCID: PMC6456288 DOI: 10.1371/journal.pone.0214406] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Accepted: 03/12/2019] [Indexed: 11/26/2022] Open
Abstract
High dimensional biomedical data contain tens of thousands of features, accurate and effective identification of the core features in these data can be used to assist diagnose related diseases. However, there are often a large number of irrelevant or redundant features in biomedical data, which seriously affect subsequent classification accuracy and machine learning efficiency. To solve this problem, a novel filter feature selection algorithm based on redundant removal (FSBRR) is proposed to classify high dimensional biomedical data in this paper. First of all, two redundant criteria are determined by vertical relevance (the relationship between feature and class attribute) and horizontal relevance (the relationship between feature and feature). Secondly, to quantify redundant criteria, an approximate redundancy feature framework based on mutual information (MI) is defined to remove redundant and irrelevant features. To evaluate the effectiveness of our proposed algorithm, controlled trials based on typical feature selection algorithm are conducted using three different classifiers, and the experimental results indicate that the FSBRR algorithm can effectively reduce the feature dimension and improve the classification accuracy. In addition, an experiment of small sample dataset is designed and conducted in the section of discussion and analysis to clarify the specific implementation process of FSBRR algorithm more clearly.
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Zhao XS, Bao LL, Ning Q, Ji JC, Zhao XW. An Improved Binary Differential Evolution Algorithm for Feature Selection in Molecular Signatures. Mol Inform 2017; 37:e1700081. [PMID: 29106044 DOI: 10.1002/minf.201700081] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 10/18/2017] [Indexed: 11/08/2022]
Abstract
The discovery of biomarkers from high-dimensional data is a very challenging task in cancer diagnoses. On the one hand, biomarker discovery is the so-called high-dimensional small-sample problem. On the other hand, these data are redundant and noisy. In recent years, biomarker discovery from high-throughput biological data has become an increasingly important emerging topic in the field of bioinformatics. In this study, we propose a binary differential evolution algorithm for feature selection. Firstly, we suggest using a two-stage approach, where three filter methods including the Fisher score, T-statistics, and Information gain are used to generate the feature pool for input to differential evolution (DE). Secondly, in order to improve the performance of differential evolution algorithm for feature selection, a new variant of binary DE called BDE is proposed. Three optimization strategies are incorporated into the BDE. The first strategy is the heuristic method in initial stage, the second one is the self-adaptive parameter control, and the third one is the minimum change value to improve the exploration behaviour thus enhance the diversity. Finally, Support vector machine (SVM) is used as the classifier in 10 fold cross-validation method. The experimental results of our proposed algorithm on some benchmark datasets demonstrate the effectiveness of our algorithm. In addition, the BDE forged in this study will be of great potential in feature selection problems.
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Affiliation(s)
- X S Zhao
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130000, P.R.China
| | - L L Bao
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130000, P.R.China
| | - Q Ning
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130000, P.R.China
| | - J C Ji
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130000, P.R.China
| | - X W Zhao
- School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130000, P.R.China.,Key Laboratory of symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
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Ma H, Simon D, Siarry P, Yang Z, Fei M. Biogeography-Based Optimization: A 10-Year Review. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2017. [DOI: 10.1109/tetci.2017.2739124] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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13
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Classification of Gene Expression Data Using Multiobjective Differential Evolution. ENERGIES 2016. [DOI: 10.3390/en9121061] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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