Fuzzy-rough feature selection based on λ-partition differentiation entropy

Fuzzy-rough set theory is proven as an effective tool for feature selection. Whilst promising, many state-of-the-art fuzzy-rough feature selection algorithms are time-consuming when dealing with the datasets which have a large quantity of features. In order to address this issue, a λ-partition differentiation entropy fuzzy-rough feature selection (LDE-FRFS) method is proposed in this paper. Such λ-partition differentiation entropy extends the concept of partition differentiation entropy from rough sets to fuzzy-rough sets on the view of a partition of the information system. In this case, it can efficiently gauge the significance of features. Experimental results demonstrate that, by such λ-partition differentiation entropy-based attribute significance, LDE-FRFS outperforms the competitors in terms of both the size of the reduced datasets and the execute time.

[1]  Feng Jiang,et al.  A relative decision entropy-based feature selection approach , 2015, Pattern Recognit..

[2]  Qinghua Hu,et al.  Information-preserving hybrid data reduction based on fuzzy-rough techniques , 2006, Pattern Recognit. Lett..

[3]  Xizhao Wang,et al.  Attributes Reduction Using Fuzzy Rough Sets , 2008, IEEE Transactions on Fuzzy Systems.

[4]  Farideh Fazayeli,et al.  Feature Selection Based on the Rough Set Theory and EM Clustering Algorithm , 2008 .

[5]  Mohamed Quafafou,et al.  Scalable Feature Selection Using Rough Set Theory , 2000, Rough Sets and Current Trends in Computing.

[6]  Qiang Shen,et al.  Centre for Intelligent Systems and Their Applications Fuzzy Rough Attribute Reduction with Application to Web Categorization Fuzzy Rough Attribute Reduction with Application to Web Categorization Fuzzy Sets and Systems ( ) – Fuzzy–rough Attribute Reduction with Application to Web Categorization , 2022 .

[7]  De-gang Chen,et al.  The Model of Fuzzy Variable Precision Rough Sets , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[8]  Qiang Shen,et al.  New Approaches to Fuzzy-Rough Feature Selection , 2009, IEEE Transactions on Fuzzy Systems.

[9]  Rafael Bello,et al.  Feature Selection Algorithms Using Rough Set Theory , 2007, Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007).

[10]  Qiang Shen,et al.  Fuzzy-Rough Sets Assisted Attribute Selection , 2007, IEEE Transactions on Fuzzy Systems.

[11]  Chenxia Jin,et al.  Feature selection with partition differentiation entropy for large-scale data sets , 2016, Inf. Sci..

[12]  Qinghua Hu,et al.  Parameterized attribute reduction with Gaussian kernel based fuzzy rough sets , 2011, Inf. Sci..

[13]  Chunru Wan,et al.  Unsupervised gene selection via spectral biclustering , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[14]  Wei Xie,et al.  Accurate Cancer Classification Using Expressions of Very Few Genes , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[15]  Qiang Shen,et al.  Fuzzy Entropy-assisted Fuzzy-Rough Feature Selection , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[16]  Qing Chang,et al.  Feature selection methods for big data bioinformatics: A survey from the search perspective. , 2016, Methods.

[17]  D. Dubois,et al.  ROUGH FUZZY SETS AND FUZZY ROUGH SETS , 1990 .

[18]  Xiao Zhang,et al.  Feature selection in mixed data: A method using a novel fuzzy rough set-based information entropy , 2016, Pattern Recognit..

[19]  Qinghua Hu,et al.  Fuzzy probabilistic approximation spaces and their information measures , 2006, IEEE Transactions on Fuzzy Systems.

[20]  Lipo Wang,et al.  Data Mining With Computational Intelligence , 2006, IEEE Transactions on Neural Networks.

[21]  Theresa Beaubouef,et al.  Rough Sets , 2019, Lecture Notes in Computer Science.

[22]  Chao-Ton Su,et al.  Multiclass MTS for Simultaneous Feature Selection and Classification , 2009, IEEE Transactions on Knowledge and Data Engineering.