[26] Main problem of SVM supplier AEB071 algorithm is constancy an uncontrollability of c parameter in relation (6). To resolve this problem, in this paper, υ-SVM algorithm has been used. This algorithm was introduced by Scholkopf in 2000.[27] In this algorithm, a pair of ωTx+ω0 = ± ρ, ρ≥0 hyper-planes, and also a new parameter named υ(0,1) has been employed. With the use of this algorithm, relation (12) is modified as below: And we have: In Scholkopf and Smola[27] it has been proved that v is an upper bound on a part of training data and a lower bound on

a part of support vectors. More details of this algorithm are in Theodoridis and Koutroumbas.[28] GENERAL STRUCTURE OF PROPOSED ALGORITHM The structure of modified SVM sub-classifier to classify DNA microarray data based on selective ICA is displayed in Figure 2. Performance details of this algorithm are as below. Figure 2 Modified support vector machine classifier structure in order to classify DNA microarray data based on ICA selective algorithm Input We indicate DNA microarray data with Xint and the number of genes that their expression level has lower oscillation among different classes with p, also, the number of ICs participating in reconstructing new samples with p, pı

< p, and the number of υ-SVM sub-classifiers with N and υ-SVM sub-classifiers having most votes with Nı. Levels of Performing Algorithm Applying Kruskal–Wallis test method to select P genes as their expression level has minor oscillation, and establishing sample set X. For i = 1:N: Applying ICA on X in order to create combination matrix A and source signal matrix S Calculating reconstruction error of P IC according to Eq. (4) Selecting p′IC which their reconstruction error is roughly low for reconstructing new sample set, Xnew Training υ-SVM sub-classifiers on Xnew and using k-fold validation method to gain ri correctness rate. The amount of k is considered to be 10.[29] End. Correctness rate of all υ-SVM sub-classifiers are displayed as r = r1,r2,···,rN; with selecting Nı first sub-classifier which have a high accuracy,

final rate of classifier accuracy ri, can be achieved. Output correctness rates related to υ-SVM sub-classifiers with highest effect and correctness rate of υ-SVM sub-classifier. All implementation levels of proposed algorithm have been carried out on a computer with 3.4 GHz processer and RAM memory of AV-951 1 GHz, also to apply υ-SVM algorithm, LIBSVM written in C++ work environment. First, by applying Kruskal–Wallis test method on data related to blood, breast and lung cancers, we selected 10, 10 and 20 effective genes in these data, respectively, with the least oscillation of their expression level. Then, FICA algorithm was applied on selected genes to extract ICs. In the third step, appropriate ICs were selected according to their reconstruction error; as we selected 6, 7, 8 and 9 ICs from first data, and 16, 17, 18 and 19 from the second data, respectively.