Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.083713
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

Boundary Region-Driven Feature Selection for Neighborhood Rough Sets

Wenchang Yu1, Xiaoqin Ma1,2, Zheqing Zhang1, Kezhong Lu1,2,*
1 School of Big Data and Artificial Intelligence, Chizhou University, Chizhou, China
2 Anhui Education Big Data Intelligent Perception and Application Engineering Research Center, Anhui Provincial Joint Construction Key Laboratory of Intelligent Education Equipment and Technology, Chizhou, China
* Corresponding Author: Kezhong Lu. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.083713

Received 09 April 2026; Accepted 01 June 2026; Published online 29 June 2026

Abstract

Feature selection grounded in neighborhood rough sets has attracted sustained research attention owing to its principled treatment of classification uncertainty. However, existing forward greedy algorithms typically evaluate uncertainty over the entire object universe at each iteration, resulting in prohibitive computational complexity on large-scale datasets. To address this inefficiency, we introduce a new uncertainty index built upon Boundary Object Sets (BOS). BOS are defined as objects whose neighborhood granules intersect with multiple decision classes, thereby capturing intrinsic classification ambiguity. The proposed measure quantifies the proportion of these boundary objects relative to the total universe size. Grounded in this measure, we develop the Boundary Object Set Feature Selection (BOSFS) algorithm. BOSFS employs a double-contraction strategy that simultaneously reduces the candidate attribute pool and eliminates consistent objects from the active working set. Consequently, the algorithm restricts computationally expensive distance calculations to the monotonically shrinking BOS. Experiments on ten benchmark datasets, evaluated against six competing algorithms under three classifiers, confirm that BOSFS achieves the highest classification accuracy in 15 of 30 test cases while consuming only 25% of the runtime of the second-fastest competitor.

Keywords

Neighborhood rough set; uncertainty measure; feature selection; boundary object set

  • 10

    View

  • 2

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link