IFSFSPIS: Incremental Feature Selection and Feature Sensitive Progressive Instance Selection Models for BigData Analytics
Keywords:
BigData Analytics, Feature Sensitive Progressive Instance Selection, Incremental Feature Selection, Select-k-BestAbstract
In this paper, a highly robust and first-of-its-kind Incremental Feature Selection (IFS) framework is designed for BigData Analytics, which considers both feature reduction as well as feature sensitive instance selection as a viable solution towards BigData Analytics. Unlike classical threshold based feature selection methods, this work is designed for an IFS model encompassing Chi-Squared IFS with Feature Sensitive Progressive Instance Selection (FSPIS). This concept intends to meet Volume, Variety, Velocity, and Veracity aspects of the BigData simultaneously. FSPIS model executed K-Means clustering over the selected features and performed incremental stratified instance selection. The proposed FSPIS model initiated feature selection with minimum volume as 20% (and maximum as 80%), which was continuously updated by appending new (ranked) features and corresponding instances to achieve expected accuracy performance. To ensure generalizability of the solution, FSPIS model can be applied to an ensemble learning model encompassing Bagging, AdaBoost, k-NN, Random Forest and Extended-Tree Classifiers as foundational-classifiers to perform consensus-based classification. Simulation results over the different datasets confirmed that the proposed FSPIS model selects minimum features while retaining higher statistical performance (i.e., Accuracy), and minimum computational time than other state-of-art techniques.
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