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A Novel Strategy for Minimum Attribute Reduction Based on Rough Set Theory and Fish Swarm Algorithm.

Yuebin Su^1,2, Jin Guo¹

¹School of Information Science and Technology, Southwest Jiao Tong University, Chengdu 610031, China.

Computational Intelligence and Neuroscience

|September 13, 2017

Summary

This summary is machine-generated.

This study introduces a new attribute reduction (AR) method using rough set theory (RST) and fish swarm algorithm (FSA). The approach effectively identifies minimal reducts for data mining preprocessing.

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Area of Science:

Data Mining
Artificial Intelligence
Machine Learning

Background:

Attribute reduction (AR) is crucial for data preprocessing in data mining.
Identifying reducts with minimal cardinality is essential for efficient AR.
Existing methods may not always find the optimal minimal reducts.

Purpose of the Study:

To propose a novel search strategy for minimal attribute reduction.
To combine rough set theory (RST) and fish swarm algorithm (FSA) for effective AR.
To enhance the efficiency and accuracy of finding minimal reducts.

Main Methods:

A coding method for attribute set subsets was developed.
Core attributes were identified using a discernibility matrix.
Fish Swarm Algorithm (FSA) was employed with a fitness function based on RST attribute dependency.
Subsets of non-core attributes were encoded as FSA individuals.

Main Results:

The proposed method effectively searches for minimal attribute reduction sets.
Experimental results on UCI datasets validate the method's performance.
The algorithm demonstrated excellent global search ability.

Conclusions:

The novel RST-FSA approach provides an effective strategy for minimal attribute reduction.
This method enhances data mining preprocessing by efficiently identifying optimal reducts.
The approach offers a robust solution for finding minimal cardinality reducts.