A Most Discriminative and Highly Informative Feature Selection Method on Multilingual Text Data

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Presented at CRYPIS 2020 by

Suman Mamidi

The presence of irrelevant features in the data leads to high dimensionality and complexity in the machine learning models. Feature selection solves the problems of high dimensionality by discarding irrelevant features from the feature space, thus reducing the model complexity and enhancing accuracy. In this paper, we define the most discriminative and high informative (MDHI) feature selection method. Discriminative information between the features is computed using clustering and similarity metrics. The information gain between the class label and feature helps to select a highly informative feature. The MDHI method is evaluated on different datasets and is compared with various state-of-art feature selection methods. The results show that this method performs better for classification tasks.