Full Citation

Title: A new approach for reduction of attributes based on stripped quotient sets

Citation Type: Journal Article

Publication Year: 2020

ISBN:

ISSN: 00313203

DOI: 10.1016/j.patcog.2019.106999

NSFID:

PMCID:

PMID:

Abstract: Attribute reduction is a key problem in many areas such as data mining, pattern recognition, machine learning. The problems of finding all reducts as well as finding a minimal reduct in a given data table have been proved to be NP-hard. Therefore, to overcome this difficulty, many heuristic attribute reduction methods have been developed in recent years. In the process of heuristic attribute reduction, accelerating calculation of attribute significance is very important, especially for big data cases. In this paper, we firstly propose attribute significance measures based on stripped quotient sets. Then, by using these measures, we design efficient algorithms for calculating core and reduct, in which the time complexity will be considered in detail. Additionally, we will also give properties directly related to efficiently computing the attribute significance and significantly reducing the data size in the process of calculation. By theoretical and experimental views, we will show that our method can perform efficiently for large-scale data sets.

Url: https://www.sciencedirect.com/science/article/pii/S0031320319303024

Url: https://linkinghub.elsevier.com/retrieve/pii/S0031320319303024

User Submitted?: No

Authors: Thuy, Nguyen Ngoc; Wongthanavasu, Sartra

Periodical (Full): Pattern Recognition

Issue:

Volume: 97

Pages: 106999

Data Collections: IPUMS USA

Topics: Other, Population Data Science

Countries: