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Access Type

WSU Access

Date of Award

January 2020

Degree Type


Degree Name



Computer Science

First Advisor

Dongxiao Zhu


Feature selection has been one of the hottest branch in machine learning and data

mining. In the past decade, high dimensional big data has proliferated in various areas,

such as text mining, bioinformatics, computer vision, e-commerce. The high dimensionality

brings great challenges for traditional statistical and machine learning techniques. Feature

selection is one effective technique handling the challenges. It can not only improve model

generalization performance and robustness, but also make model interpretable to better

understand the underlying mechanism of data generation.

In this thesis, we specifically study the multi-class classification problems using regularized multinomial logistic regression where the number of features is large and intrinsic

group structure of features exists. For multi-class classification, different classes might have

different related features and feature groups. Our goal is to improve mdoel performance by

incorporating the group information in the model and simultaneously achieving sparsity at

the feature and feature group level. Hence, we propose a class-conditional regularization

method for multinomial logistic regression for this purpose. For model optimization, an

efficient cyclic block coordinate descent algorithm is developed. Finally, we evaluate our

method on real-world datasets to demonstrate its effectiveness.

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