IDENTIFICATION OF SIGNIFICANT FEATURES USING RANDOM FOREST FOR HIGH DIMENSIONAL MICROARRAY DATA

Feature subset selection for microarray data aims at reducing the number of genes so that useful information can be extracted from the samples. At the same time, selecting the relevant genes (features) from the high dimensional data can improve the classification accuracy of the learning algorithm. This paper proposes a feature selection algorithm, which is fit for high dimensional and small sample size microarray data. Feature selection is performed in two phases. In the first phase, Random Forest is used to identifying the importance of each feature, so that the features with high relevance can be given priority over less relevant ones. In the second phase, feature clustering is performed around the relevant features to yield the reduced feature set. A statistical method is used to create the clusters that aid in giving the genes specifically representing the disease. The effectiveness of the proposed algorithm has been compared with three state-of-the-art feature selection algorithms viz. Fast-Correlation Based Filter (FCBF), a Fast Clustering-Based Feature Selection Algorithm (FAST) and Random Forest (RF) on nine real-world cancer microarray datasets. Empirically, the algorithms have been evaluated through three well-known classifiers viz. probability based Naive Bayes, Tree-based C4.5, and the Instance-based IB1. The stated result shows that the proposed algorithm can be helpful in finding the smaller set of features for cancer microarray datasets with better classification accuracy.