ABC optimized RBF network for classification of EEG signal for epileptic seizure identification

AbstractThe brain signals usually generate certain electrical signals that can be recorded and analyzed for detection in several brain disorder diseases. These small signals are expressly called as Electroencephalogram (EEG) signals. This research work analyzes the epileptic disorder in human brain through EEG signal analysis by integrating the best attributes of Artificial Bee Colony (ABC) and radial basis function networks (RBFNNs). We have used Discrete Wavelet Transform (DWT) technique for extraction of potential features from the signal. In our study, for classification of these signals, in this paper, the RBFNNs have been trained by a modified version of ABC algorithm. In the modified ABC, the onlooker bees are selected based on binary tournament unlike roulette wheel selection of ABC. Additionally, kernels such as Gaussian, Multi-quadric, and Inverse-multi-quadric are used for measuring the effectiveness of the method in numerous mixtures of healthy segments, seizure-free segments, and seizure segments. Our experimental outcomes confirm that RBFNN with inverse-multi-quadric kernel trained with modified ABC is significantly better than RBFNNs with other kernels trained by ABC and modified ABC

Feature selection model based on clustering and ranking in pipeline for microarray data

Most of the available feature selection techniques in the literature are classifier bound. It means a group of features tied to the performance of a specific classifier as applied in wrapper and hybrid approach. Our objective in this study is to select a set of generic features not tied to any classifier based on the proposed framework. This framework uses attribute clustering and feature ranking techniques in pipeline in order to remove redundant features. On each uncovered cluster, signal-to-noise ratio, t-statistics and significance analysis of microarray are independently applied to select the top ranked features. Both filter and evolutionary wrapper approaches have been considered for feature selection and the data set with selected features are given to ensemble of predefined statistically different classifiers. The class labels of the test data are determined using majority voting technique. Moreover, with the aforesaid objectives, this paper focuses on obtaining a stable result out of various classification models. Further, a comparative analysis has been performed to study the classification accuracy and computational time of the current approach and evolutionary wrapper techniques. It gives a better insight into the features and further enhancing the classification accuracy with less computational time

EEG signal classification using PSO trained RBF neural network for epilepsy identification

The electroencephalogram (EEG) is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT). To classify the EEG signal, we used a radial basis function neural network (RBFNN). As shown herein, the network can be trained to optimize the mean square error (MSE) by using a modified particle swarm optimization (PSO) algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learnin

Multi-objective clustering: a kernel based approach using Differential Evolution

A multi-objective algorithm is always favoured over a single objective algorithm as it considers different aspects of a dataset in the form of various objectives. In this article, a multi-objective clustering algorithm has been proposed based on Differential Evolution. Here, three objectives have been considered to handle different complex datasets. In addition to this, a kernel function is hybridised with the objectives to evaluate the data on a hyperspace for reducing the impact of nonlinearity on cluster formation. Moreover, to get the best compromised solution from the Pareto front an effective fuzzy concept has been followed. Five metaheuristic approaches have been taken into consideration for performance comparison. These methodologies have been applied to twelve datasets and the result reveals the efficacy of the proposed model in data clustering

Elitism-based multi-objective differential evolution with extreme learning machine for feature selection: a novel searching technique

The features related to the real world data may be redundant and erroneous in nature. The vital role of feature selection (FS) in handling such type of features cannot be ignored in the area of computational learning. The two most commonly used objectives for FS are the maximisation of the accuracy and minimisation of the number of features. This paper presents an Elitism-based Multi-objective Differential Evolution algorithm for FS and the novelty lies in the searching process which uses Minkowski Score (MS) and simultaneously optimises three objectives. The MS is considered as the third objective to keep track of the feature subset which is capable enough to produce a good classification result even if the average accuracy is poor. Extreme Learning Machine because of its fast learning speed and high efficiency has been considered with this multi-objective approach as a classifier for FS. Twenty-one benchmark datasets have been considered for performance evaluation. Moreover, the selected feature subsets are tested using 10-fold cross-validation. A comparative analysis of the proposed approach with two classical models, three single objective algorithms, and four multi-objective algorithms has been carried out to test the efficacy of the model