Data-driven fuzzy rule generation and its application for student academic performance evaluation

Several approaches using fuzzy techniques have been proposed to provide a practical method for evaluating student academic performance. However, these approaches are largely based on expert opinions and are difficult to explore and utilize valuable information embedded in collected data. This paper proposes a new method for evaluating student academic performance based on data-driven fuzzy rule induction. A suitable fuzzy inference mechanism and associated Rule Induction Algorithm is given. The new method has been applied to perform Criterion-Referenced Evaluation (CRE) and comparisons are made with typical existing methods, revealing significant advantages of the present work. The new method has also been applied to perform Norm-Referenced Evaluation (NRE), demonstrating its potential as an extended method of evaluation that can produce new and informative scores based on information gathered from data

A weighted subsethood mamdani fuzzy rules based system rule extraction (MFRBS-WSBA) for forecasting electricity load demand – a framework

Fuzzy rules are very important elements that should be taken consideration seriously when applying any fuzzy system.This paper proposes the framework of Mamdani Fuzzy Rulebased System with Weighted Subsethood-based Algorithm (MFRBS-WSBA) for forecasting electricity load demand. Specifically, this paper proposed two frameworks: MFRBSWSBA and WSBA framework where the WSBA is embedded in MFRBS-WSBA (fourth step in MFRBS-WSBA).The objective of this paper is to show the fourth step in the MFRBS-WSBA framework which applied the new electricity load forecasting rule extraction by WSBA method.We apply the proposed WSBA framework in Malaysia electricity load demand data as a numerical example in this paper.These preliminary results show that the WSBA framework can be one of alternative methods to extract fuzzy rules for forecast electricity load demand where the proposed method provide a simple to interpret the fuzzy rules and also offer a new direction to interpret the fuzzy rules compared to classical fuzzy rule

A Weighted Subsethood Mamdani Fuzzy Rules Based System Rule Extraction (MFRB-WSBA) for Forecasting Electricity Load Demand - A Framework

Fuzzy rules are very important elements that should be taken consideration seriously when applying any fuzzy system. This paper proposes the framework of Mamdani Fuzzy Rulebased System with Weighted Subsethood-based Algorithm (MFRBS-WSBA) for forecasting electricity load demand. Specifically, this paper proposed two frameworks: MFRBSWSBA and WSBA framework where the WSBA is embedded in MFRBS-WSBA (fourth step in MFRBS-WSBA). The objective of this paper is to show the fourth step in the MFRBS-WSBA framework which applied the new electricity load forecasting rule extraction by WSBA method. We apply the proposed WSBA framework in Malaysia electricity load demand data as a numerical example in this paper. These preliminary results show that the WSBA framework can be one of alternative methods to extract fuzzy rules for forecast electricity load demand where the proposed method provide a simple to interpret the fuzzy rules and also offer a new direction to interpret the fuzzy rules compared to classical fuzzy rule