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Feature selection using mutual information based uncertainty measures for tumor classification.

Lin Sun¹, Jiucheng Xu

¹International WIC Institute, Beijing University of Technology, Beijing, China College of Computer and Information Engineering, Henan Normal University, Xinxiang, China.

Bio-Medical Materials and Engineering

|November 12, 2013

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This study introduces a novel neighborhood rough set-based feature selection method for tumor classification. The approach effectively identifies relevant genes, improving cancer classification accuracy with efficient algorithms.

Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

Feature selection is crucial for accurate tumor classification.
Existing methods may lack efficiency or effectiveness in high-dimensional biological data.
Neighborhood rough sets offer a promising framework for handling data uncertainty.

Purpose of the Study:

To develop an efficient and effective feature selection algorithm for tumor classification using neighborhood rough sets.
To introduce and investigate novel uncertainty measures within the neighborhood rough set framework.
To evaluate the proposed algorithm on real-world cancer classification tasks.

Main Methods:

Introduction of neighborhood entropy, conditional neighborhood entropy, neighborhood mutual information, and neighborhood conditional mutual information.

Keywords:

Feature selection mutual information neighborhood rough set tumor classification

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Investigation of properties and relationships among these novel uncertainty measures.

Development of a feature selection algorithm using improved Minimal-Redundancy-Maximal-Relevancy (mRMR) and a sequential forward greedy search strategy.

Main Results:

The proposed algorithm demonstrates low time complexity.
Experimental results on several cancer classification tasks show high efficiency and effectiveness.
The introduced uncertainty measures successfully evaluate gene relevance for tumor classification.

Conclusions:

The neighborhood rough set-based feature selection approach is a viable and powerful tool for tumor classification.
The novel uncertainty measures and the mRMR-based algorithm contribute to improved cancer classification accuracy.
This method offers a computationally efficient solution for gene selection in cancer research.