Recent trends in data mining and machine learning focus on knowledge extraction and explanation, to make crucial decisions from data, but data is virtually enormous in size and mostly associated with noise. Neuro-fuzzy systems are most suitable for representing knowledge in a data-driven environment. Many neuro-fuzzy systems were proposed for feature selection and classification; however, they focus on quantitative (accuracy) than qualitative (transparency). Such neuro-fuzzy systems for feature selection and classification include Enhance Neuro-Fuzzy (ENF) and Adaptive Dynamic Clustering Neuro-Fuzzy (ADCNF). Here a neuro-fuzzy system is proposed for feature selection and classification with improved accuracy and transparency. The novelty of the proposed system lies in determining a significant number of linguistic features for each input and in suggesting a compelling order of classification rules using the importance of input feature and the certainty of the rules. The performance of the proposed system is tested with 8 benchmark datasets. 10-fold cross-validation is used to compare the accuracy of the systems. Other performance measures such as false positive rate, precision, recall, f-measure, Matthews correlation coefficient and Nauck's index are also used for comparing the systems. It is observed from the experimental results that the proposed system is superior to the existing neuro-fuzzy systems.
Feature selection for probabilistic load forecasting via sparse penalized quantile regression
