Abstract
This study proposes Online Kernel Adaptive Multilayer Generalized Learning Vector Quantization (KAMGLVQ) for handling imbalanced data sets. KAMGLVQ is extended version of AMGLVQ that used kernel function to handling non-linear classification problems. Basically AMGLVQ is vector quantization based learning. The vector quantization based learning is very simple algorithm that can be applied to the multiclass problem and the complexity of LVQ can be controlled during training process. KAMGLVQ works at online kernel learning system that integrating feature extraction and classification. The architecture network of KAMGLVQ consists of three layers, input layer, hidden layer, and an output layer. The hidden layer of KAMGLVQ is adaptive; this algorithm will generate a number of hidden layer nodes. The algorithm implement on real ECG signals from the MIT-BIH arrhythmias database and synthetic data. The experiments showed that KAMGLVQ able improve the accuracy of classification better than SVM or back-propagation NN; also able to reduce the time computational cost.
Highlights
The imbalanced data set is a classification problem because almost learning algorithm is designed for the balanced dataset
Kernel Adaptive Multilayer Generalized Learning Vector Quantization (KAMGLVQ) is extended version of Adaptive Multilayer Generalized Learning vector Quantization (AMGLVQ) that used kernel function to handling non-linear classification problems
Online KAMGLVQ is a non-linear model of AMGLVQ that proposed in the previous study [5]
Summary
The imbalanced data set is a classification problem because almost learning algorithm is designed for the balanced dataset. The proposed Online Kernel Adaptive Generalized learning Vector Quantization (KAMGLVQ) to handle the imbalanced dataset by using combining external approach and internal approach. AMGLVQ have been handling an imbalanced dataset, inconsistency between feature extraction and classification problem, but in some case that non-linear, the boundary class that has been created is not smooth. Many algorithms have been proposed to automatically classify life-threatening arrhythmia beat types from ECG data They are focus on feature extraction [13], [14] and some of researcher focus on classification process [15], [16]. Online KAMGLVQ is a non-linear model of AMGLVQ that proposed in the previous study [5] This algorithm used the integrating model of feature extraction and classification. The testing data that out of boundary class output in the testing phase, will be created a new class as unclassifiable heartbeat and this class is an increment, depend on how far the differences in the data
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