(ii) The SVM is a powerful machine-learning algorithm developed from statistical learning theory (Vapnik, 1998) that has been successfully applied in many fields for data classification.
The random forest (RF) algorithm [ 12] is a classification algorithm that uses an ensemble of tree-structured classifiers, which has been used successfully in many applications for data classification and achieves high performance.
Efficient organization and manipulation of this data are required for many tasks, such as data classification for storage or navigation, differential processing according to content, searching for specific information, and many others.
Many methods for molecular data classification or clustering based on gene expression data have appeared in this area [ 2– 14].
The use of soft computing technique for the performance modeling plays a vital role due to its critical utility in decision analysis, automatic control, data classification, and many more.
There are many data mining tools such as classification, clustering, association rule mining, attribute selection and visual interactive page (Kaya et al. 2014).
They have been studied extensively in the field of machine learning and applied to many data processing problems such as image classification and biological DNA identification [61, 62]. 6.
Finally, PROFESS provides a great opportunity as the source data for many recent novel data mining and data classification algorithms that are especially designed for large-scale biological data (59).
Using the proposed method yields 100% classification accuracy in many data sets, but it utilizes a large number of selected genes (large gene subset) to obtain the high accuracy.
Many of the contemporary databases used in data classification research [1 10] uses considerably large number of data points to represent an object sample.
