Comparison of feature selection methods in text classification on highly skewed datasets
Muhammad Nabeel Asim, Muhammad Wasim, Muhammad Sajid Ali, Abdur Rehman · 2017
Feature selection plays a vital role in boosting the performance of a classifier. The aim of feature selection is to remove irrelevant features and choose only highly invidious ones thus improving the performance of classification. This paper compares the performance of nine popular feature ranking (FR) metrics on six benchmark datasets using Naive Bayes (NB) and Support Vector Machine (SVM) classifiers. Results of comparisons are shown both in tabular and graphical forms. The nine FR metrics include: Chi squared (CHI), Odds Ratio (OR), Information Gain (IG), Gini Index (GINI), Poisson Ratio (POIS), Normalized Difference Measure (NDM), Balanced Accuracy Measure (ACC2), Distinguishing Feature Selector (DFS), and Binormal Separation (BNS). We used six datasets for empirical evaluation: WAP, K1a, RE0, RE1, Spam and Enron where two datasets (RE0, K1a) are highly skewed. The performance of classifiers is evaluated based on popular micro and macro-averaged F1 measures.