Feature Selection and Classification of Microarray Data using MapReduce based ANOVA and K-Nearest Neighbor
Abstract— Feature Selection and Classification of Microarray Data using MapReduce based ANOVA and K-Nearest Neighbor. The major drawback of microarray data is the ‘curse of dimensionality problem’, this hinders the useful information of dataset and leads to computational instability. Therefore, selecting relevant genes is an imperative in microarray data analysis. Most of the existing schemes employ a two-phase processes: feature selection/extraction followed by classification. A statistical test, ANOVA based on MapReduce is proposed to select the relevant features. After feature selection, MapReduce based K-Nearest Neighbor < Final Year Projects 2016 > K-NN classifier is also proposed to classify the microarray data. These algorithms are successfully implemented on Hadoop framework and comparative analysis is done using various datasets.
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