a novel neuro-fuzzy classification technique for data mining

Clicks: 143
ID: 246426
2014
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Ranked #17 of 21 articles by views in restorative neurology and neuroscience

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Abstract
In our study, we proposed a novel Neuro-fuzzy classification technique for data mining. The inputs to the Neuro-fuzzy classification system were fuzzified by applying generalized bell-shaped membership function. The proposed method utilized a fuzzification matrix in which the input patterns were associated with a degree of membership to different classes. Based on the value of degree of membership a pattern would be attributed to a specific category or class. We applied our method to ten benchmark data sets from the UCI machine learning repository for classification. Our objective was to analyze the proposed method and, therefore compare its performance with two powerful supervised classification algorithms Radial Basis Function Neural Network (RBFNN) and Adaptive Neuro-fuzzy Inference System (ANFIS). We assessed the performance of these classification methods in terms of different performance measures such as accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, precision, recall, and f-measure. In every aspect the proposed method proved to be superior to RBFNN and ANFIS algorithms.
Reference Key
ghosh2014egyptiana Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Soumadip Ghosh;Sushanta Biswas;Debasree Sarkar;Partha Pratim Sarkar
Journal restorative neurology and neuroscience
Year 2014
DOI
10.1016/j.eij.2014.08.001
URL
http://www.sciencedirect.com/science/article/pii/S1110866514000292
Keywords
Data mining Neural network classification computer science

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