A relative decision entropy-based feature selection approach. Rough set theory has been proven to be an effective tool for feature selection. To avoid the exponential computation in exhaustive methods, many heuristic feature selection algorithms have been proposed in rough sets. However, these algorithms still suffer from high computational cost. In this paper, we propose a novel heuristic feature selection algorithm (called FSMRDE) in rough sets. To measure the significance of features in FSMRDE, we propose a new model of relative decision entropy, which is an extension of Shannon’s information entropy in rough sets. Moreover, to test the effectiveness of FSMRDE, we apply it to intrusion detection and other application domains. Experimental results show that by using the relative decision entropy-based feature significance as heuristic information, FSMRDE is efficient for feature selection. In particular, FSMRDE is able to achieve good scalability for large data sets.
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Jun, Wang; Lingyu, Tang; Xianyong, Zhang; Yuyan, Luo: Three-way weighted combination-entropies based on three-layer granular structures (2017)
- Zhang, Xianyong; Miao, Duoqian: Three-way attribute reducts (2017)
- Jiang, Feng; Liu, Guozhu; Du, Junwei; Sui, Yuefei: Initialization of (K)-modes clustering using outlier detection techniques (2016)
- Jiang, Sheng-yi; Wang, Lian-xi: Efficient feature selection based on correlation measure between continuous and discrete features (2016)
- Li, Fachao; Zhang, Zan; Jin, Chenxia: Feature selection with partition differentiation entropy for large-scale data sets (2016)
- Teng, Shu-Hua; Lu, Min; Yang, A-Feng; Zhang, Jun; Nian, Yongjian; He, Mi: Efficient attribute reduction from the viewpoint of discernibility (2016)
- Jiang, Feng; Sui, Yuefei; Zhou, Lin: A relative decision entropy-based feature selection approach (2015)