An Efficient Gaussian Kernel Based Fuzzy-Rough Set Approach for Feature Selection
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Updated
Jun 25, 2018 - R
An Efficient Gaussian Kernel Based Fuzzy-Rough Set Approach for Feature Selection
A small demo Python implementation of some (fuzzy) roughset algorithms such as LEM2
Code for 3 papers: 1) "Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets"; 2) "LT3 at SemEval-2022 Task 6: Fuzzy-Rough Nearest neighbor Classification for Sarcasm Detection"; 3) "Fuzzy Rough Nearest Neighbour Methods for Detecting Emotions, Hate Speech and Irony" by O. Kaminska, Ch. Cornelis and V. Hoste.
This is an implementation of SUFFUSE: Simultaneous Fuzzy-Rough Feature-Sample Selection
The code for the paper "Fuzzy Rough Nearest Neighbour Methods for Aspect-Based Sentiment Analysis" by O. Kaminska, C. Cornelis, and V. Hoste published to the Electronics journal, particularly, the special issue AI for Text Understanding (Volume 12, 2023)
This is an implementation of Delta Quick Reduct algorithm
This is an implementation of improved Fuzzy Rough QuickReduct algorithm
This is an implementation of Fuzzy Rough feature selection based on binary Shuffled Frog Leaping Algorithm
This is an implementation of Fuzzy Rough Dependency Degree (FRDD) to calculate the importance of selected feautres
This is an implementation of Fuzzy Rough QuickReduct algorithm
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