Reference: Nimagna Biswas, Saurajit Chakraborty, Sankha Subhra Mullick, and Swagatam Das, A Parameter Independent Fuzzy Weighted k-Nearest Neighbor Classifier, Pattern Recognition Letters, November, 2017.
Contact: [email protected] (Nimagna Biswas), [email protected] (Saurajit Chakraborty).
- The package contains 9 functions, 1 script and 1 sample dataset from UCI repository [1].
- pifwknn.m: The main script.
- shade.m: Implementation of SHADE [2].
- fitness.m. Calculates the leave-one-ot error for a value of k and set of class specific feature weights.
- wtdistance.m: Claculates weighted distance as described in the corresponding article.
- membership_assignment.m: Calculates the fuzzy membership matrix.
- fuzzy_knn.m: Supporting function.
- extract.m: Supporting function.
- wt_Mean.m: Supporting function.
- wt_Lehmer_Mean.m: Supporting function.
- cauchy_rand.m: Supporting function.
- Balance_Scale.mat: Example dataset.
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DEPENDENCIES:
- MATLAB 2014a and above.
- The source code and data files must be in the same folder.
- Load the dataset (.mat format) in the workspace and run the script named 'pifwknn.m'.
- Please read 'pifwknn.m' (or Balance_Scale.mat) for further arrangement of the dataset.
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REFERENCE:
- [1] Lichman, M., 2013. UCI machine learning repository. URL: http:https://archive.ics.uci.edu/ml.
- [2] Tanabe, R., Fukunaga, A., 2013. Success-history based parameter adaptation for differential evolution, in: IEEE CEC, pp. 71–78.