Cancer Classification in Microarray Data using a Hybrid Selective Independent Component Analysis and ϑ-Support Vector Machine Algorithm

Abstract

Microarray data have an important role in identification and classification of the cancer tissues. Having a few samples of microarrays in cancer researches is always one of the most concerns which lead to some problems in designing the classifiers. For this matter, preprocessing gene selection techniques should be utilized before classification to remove the noninformative genes from the microarray data. An appropriate gene selection method can significantly improve the performance of cancer classification. In this paper, we use selective independent component analysis (SICA) for decreasing the dimension of microarray data. Using this selective algorithm, we can solve the instability problem occurred in the case of employing conventional independent component analysis (ICA) methods. First, the reconstruction error and selective set are analyzed as independent components of each gene, which have a small part in making error in order to reconstruct new sample. Then, some of the modified support vector machine (υ-SVM) algorithm sub-classifiers are trained, simultaneously. Eventually, the best sub-classifier with the highest recognition rate is selected. The proposed algorithm is applied on three cancer datasets (leukemia, breast cancer and lung cancer datasets), and its results are compared with other existing methods. The results illustrate that the proposed algorithm (SICA + υ-SVM) has higher accuracy and validity in order to increase the classification accuracy. Such that, our proposed algorithm exhibits relative improvements of 3.3% in correctness rate over ICA + SVM and SVM algorithms in lung cancer dataset.

Automatic recognition of acute myelogenous leukemia in blood microscopic images using K-means clustering and support vector machine

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Independent components analysis (ICA) at the “cocktail-party” in analytical chemistry

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An improved rotation forest algorithm based on heterogeneous classifiers ensemble for classifying gene expression profile

“…In the past years, Decision tree (DT) [5,6], Artificial neural network (ANN)[7], Bayesian networks [8],K-nearest neighbor (KNN) [9,10] and Support vector machine (SVM) [11][12][13][14] were widely used in gene expression profile classification. However, these methods always cannot obtain better classification performance because of small samples and high dimension of gene expression profile.…”