A new approach for training Lagrangian twin support vector machine via unconstrained convex minimization

Lagrangian Regularized Twin Extreme Learning Machine for Supervised and Semi-Supervised Classification

Twin extreme learning machine (TELM) is a phenomenon of symmetry that improves the performance of the traditional extreme learning machine classification algorithm (ELM). Although TELM has been widely researched and applied in the field of machine learning, the need to solve two quadratic programming problems (QPPs) for TELM has greatly limited its development. In this paper, we propose a novel TELM framework called Lagrangian regularized twin extreme learning machine (LRTELM). One significant advantage of our LRTELM over TELM is that the structural risk minimization principle is implemented by introducing the regularization term. Meanwhile, we consider the square of the l2-norm of the vector of slack variables instead of the usual l1-norm in order to make the objective functions strongly convex. Furthermore, a simple and fast iterative algorithm is designed for solving LRTELM, which only needs to iteratively solve a pair of linear equations in order to avoid solving two QPPs. Last, we extend LRTELM to semi-supervised learning by introducing manifold regularization to improve the performance of LRTELM when insufficient labeled samples are available, as well as to obtain a Lagrangian semi-supervised regularized twin extreme learning machine (Lap-LRTELM). Experimental results on most datasets show that the proposed LRTELM and Lap-LRTELM are competitive in terms of accuracy and efficiency compared to the state-of-the-art algorithms.

Universum based Lagrangian twin bounded support vector machine to classify EEG signals

BACKGROUND AND OBJECTIVE

The detection of brain-related problems and neurological disorders like epilepsy, sleep disorder, and so on is done by using electroencephalogram (EEG) signals which contain noisy signals and outliers. Universum data contains a set of a sample that does not belong to any of the concerned classes and serves as the advanced knowledge about the data distribution. Earlier information has been utilized viably in improving classification performance. Recently a novel universum support vector machine (USVM) was proposed for EEG signal classification and further, a universum twin support vector machine (UTWSVM) was proposed based on USVM to improve the performance. Inspired by USVM and UTWSVM, this paper suggests a novel method called universum based Lagrangian twin bounded support vector machine (ULTBSVM), where universum data is utilized to incorporate the prior information about the data distribution to classify healthy and seizure EEG signals.

METHODS

In the proposed ULTBSVM the square of the 2-norm of the slack variables is used to formulate the objective function strongly convex; hence it always gives unique solutions. Unlike twin support vector machine (TWSVM) and universum twin support vector machine (UTWSVM), the proposed ULTBSVM is having regularization terms that follow the structural risk minimization (SRM) principle and enhance the stability in the dual formulations, make the model well-posed and prevents the overfitting problem. Here, interracial EEG data have been considered as universum data to classify healthy and seizure signals. Several feature extraction techniques have been implemented to get important noiseless features.

RESULTS

Several EEG datasets, as well as publicly available UCI datasets, are utilized to assess the performance of the proposed method. An analytical comparison has been performed of the proposed method with USVM and UTWSVM to detect seizure and healthy signals and for real-world data, the ULTBSVM is compared with the universum based models as well as TWSVM and the proposed method gives better results in most of the cases as compared to the other methods.

CONCLUSION

The results clearly show that ULTBSVM is a potential method for the classification of EEG signals as well as real-world datasets having interracial data as universum data. Here we have used universum points for the binary class classification problem, but one can extend and use it for multi-class classification problems as well.

Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images

The emergence of deep-learning methods in different computer vision tasks has proved to offer increased detection, recognition or segmentation accuracy when large annotated image datasets are available. In the case of medical image processing and computer-aided diagnosis within ultrasound images, where the amount of available annotated data is smaller, a natural question arises: are deep-learning methods better than conventional machine-learning methods? How do the conventional machine-learning methods behave in comparison with deep-learning methods on the same dataset? Based on the study of various deep-learning architectures, a lightweight multi-resolution Convolutional Neural Network (CNN) architecture is proposed. It is suitable for differentiating, within ultrasound images, between the Hepatocellular Carcinoma (HCC), respectively the cirrhotic parenchyma (PAR) on which HCC had evolved. The proposed deep-learning model is compared with other CNN architectures that have been adapted by transfer learning for the ultrasound binary classification task, but also with conventional machine-learning (ML) solutions trained on textural features. The achieved results show that the deep-learning approach overcomes classical machine-learning solutions, by providing a higher classification performance.