A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data

Abstract

Feature (gene) selection and classification of microarray data are the two most interesting machine learning challenges. In the present work two existing feature selection/extraction algorithms, namely independent component analysis (ICA) and fuzzy backward feature elimination (FBFE) are used which is a new combination of selection/extraction. The main objective of this paper is to select the independent components of the DNA microarray data using FBFE to improve the performance of support vector machine (SVM) and Naïve Bayes (NB) classifier, while making the computational expenses affordable. To show the validity of the proposed method, it is applied to reduce the number of genes for five DNA microarray datasets namely; colon cancer, acute leukemia, prostate cancer, lung cancer II, and high-grade glioma. Now these datasets are then classified using SVM and NB classifiers. Experimental results on these five microarray datasets demonstrate that gene selected by proposed approach, effectively improve the performance of SVM and NB classifiers in terms of classification accuracy. We compare our proposed method with principal component analysis (PCA) as a standard extraction algorithm and find that the proposed method can obtain better classification accuracy, using SVM and NB classifiers with a smaller number of selected genes than the PCA. The curve between the average error rate and number of genes with each dataset represents the selection of required number of genes for the highest accuracy with our proposed method for both the classifiers. ROC shows best subset of genes for both the classifier of different datasets with propose method.

The Effect of Evolutionary Algorithm in Gene Subset Selection for Cancer Classification

“…Though, Leukemia3 and Colon cancer classification performances are a bit lower compared to that of other three datasets, they are still capable to be classified with only one and two misclassifications respectively. With comparison to the study reported in [4,12], the proposed study has obtained little bit higher accuracy which is 90.47% for colon cancer dataset whereas it is 90.32% with 3 genes in former and 90.09% with 30 genes in later. Further, in the classification of colon cancer, a sparse representation based method is proposed in [3] which provide 91.94% accuracy; nevertheless with a very huge gene subset.…”

“…Several approaches have been already carried out for cancer classification in the past decades. Some methods provide poor performance in terms of time complexity [1,2] whereas few approaches result in a huge gene subset [3,4]. Selecting a smallest informative gene subset which can predict an unknown sample perfectly is still challenging.…”

Independent component analysis provides clinically relevant insights into the biology of melanoma patients

“…While the ICA-based feature extraction method has been previously discussed (e.g. (Aziz, Verma et al, 2016, Teschendorff et al, 2007), no studies have been devoted, to our knowledge, to estimating patient prognosis using ICA-based data deconvolution. We combined weights of several significant components into a hazard score, for which a high predictive power was shown both in the reference cohort (460 patients with known survival status) and in the independent validation cohort (44 patients).…”

Artificial neural network classification of microarray data using new hybrid gene selection method

“…A sequential feature extraction approach was proposed to select the best subset of independent component for Naïve Bayes classification of microarray data (Fan et al, 2009). In our previous research paper a fuzzy-based feature selection is applied (Aziz et al, 2016) in order to solve this problem, as a hybrid approach to reduce the dimensionality of microarray data sets for SVM and NB classifiers. In Aziz et al (2015aAziz et al ( , 2015b, the author used different feature subset selection method for ICA vector to improve the performances of SVM and NB classifier.…”

“…In our previous research paper a fuzzy-based feature selection is applied (Aziz et al, 2016) in order to solve this problem, as a hybrid approach to reduce the dimensionality of microarray data sets for SVM and NB classifiers. In Aziz et al (2015aAziz et al ( , 2015b, the author used different feature subset selection method for ICA vector to improve the performances of SVM and NB classifier. Literature review reveals that there is still a scope of further improvement in the performance of classification based on gene expression profiles by using a proper choice of feature selection/extraction method combined with different classifier.…”