Dissertation/Thesis Abstract

Feature Selection through Visualisation for the Classification of Online Reviews
by Koka, Keerthika, M.S., Purdue University, 2017, 66; 10278441
Abstract (Summary)

The purpose of this work is to prove that the visualization is at least as powerful as the best automatic feature selection algorithms. This is achieved by applying our visualization technique to the online review classication into fake and genuine reviews. Our technique uses radial chart and the color overlaps to explore the best feature selection through visualization for classication. Every review is treated as a radial translucent red or blue membrane with its dimensions determining the shape of the membrane. This work also shows how the dimension ordering and combination is relevant in the feature selection process. In brief, the whole idea is about giving a structure to each text review based on certain attributes, comparing how different or how similar the structure of the different or same categories are and highlighting the key features that contribute to the classication the most. Colors and saturations aid in the feature selection process. Our visualization technique helps the user get insights into the high dimensional data by providing means to eliminate the worst features right away, pick some best features without statistical aids, understand the behavior of the dimensions in different combinations. This work outlines the different approaches explored, results and analysis.

Indexing (document details)
Advisor: Fang, Shiaofen
Commitee: Durresi, Arjan, Fang, Shiaofen, Xia, Yuni
School: Purdue University
Department: Computer and Information Technology
School Location: United States -- Indiana
Source: MAI 56/06M(E), Masters Abstracts International
Subjects: Information science, Computer science
Keywords: Data classification, Data visualisation, Multi-dimensional data visualisation, Online reviews, Text visual analytics, Visual feature selection
Publication Number: 10278441
ISBN: 978-0-355-18825-7
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