Algorithms for Feature Selection

Abstract

With the advancement of science and technology, data has increased both in sam- ple size and dimension. Examples of high-dimensional data include genomic data, text data, image retrieval, bioinformatics, etc. One of the major problems in handling such data is that all the features are not equally important. Hence, fea- ture engineering, feature selection and feature reduction are considered important pre-processing tasks to discard redundant, irrelevant features while preserving the prominent features of the data as much as possible. Feature selection, in practice, often improves the accuracy of down-stream machine learning problems, including clustering and classification. In this thesis, we aim to devise some novel and robust feature selection mecha- nisms in diverse domains of applications with a special focus on high dimensional biological data such as gene expression and single cell transcriptomic data. We develop a series of feature selection techniques equipped with structure-aware data sampling at its core. We adopt several concepts from statistics (e.g. copula and its variant), information theory (entropy), and advanced machine learning domain (variational graph autoencoder, generative adversarial network, and its variant) to design the feature selection models for high dimensional and noisy data. The proposed models perform extremely well both in supervised and unsu- pervised cases, even if the sample size is very low. Important outcomes from all the proposed methods are discussed in chapters. Moreover, an overall discussion about the applicability along with a brief mention of the shortcomings of all the discussed methods is provided. Some suggestions and guidance are provided to overcome the disadvantages which direct the future scope of improvement of all the devised methods.