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Feature Selection Considering Multiple Correlations Based on Soft Fuzzy Dominance Rough Sets for Monotonic Classification

粗集 特征选择 人工智能 数据挖掘 模式识别(心理学) 计算机科学 稳健性(进化) 模糊逻辑 基于优势度的粗糙集方法 模糊集 机器学习 单调函数 数学 化学 数学分析 基因 生物化学
作者
Binbin Sang,Hongmei Chen,Lei Yang,Jihong Wan,Tianrui Li,Weihua Xu
出处
期刊:IEEE Transactions on Fuzzy Systems [Institute of Electrical and Electronics Engineers]
卷期号:30 (12): 5181-5195 被引量:15
标识
DOI:10.1109/tfuzz.2022.3169625
摘要

Monotonic classification is a common task in the field of multicriteria decision-making, in which features and decision obey a monotonic constraint. The dominance-based rough set theory is an important mathematical tool for knowledge acquisition in monotonic classification tasks (MCTs). However, existing dominance-based rough set models are very sensitive to noise information, and only a misclassified sample will lead to large errors in acquiring knowledge. This unstable phenomenon does not meet the requirements of practical applications. On the other hand, feature selection is supposedly an effective dimensionality reduction approach for classification tasks. In the real world, feature combinations with multiple correlations can often provide important classification information, where the multiple correlations include redundancy, complementarity, and interaction between features. To the best of our knowledge, most of the existing feature selection methods for MCTs only consider the relevance between features and decision, while ignoring the multiple correlations. To overcome these two drawbacks, in this article, we propose a robust fuzzy dominance rough set model, and develop a feature selection method that considers multiple correlations based on the robust model for MCTs. First, a soft fuzzy dominance rough set (SFDRS) with robustness is proposed. Second, a feature evaluation index considering multiple correlations is presented. Finally, a feature selection algorithm based on SFDRS is designed to select an optimal feature subset. Extensive experiments are conducted on 12 public datasets, and the results show that the SFDRS model has good robustness and the proposed feature selection algorithm has excellent classification performance.
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