Antecedent selection in fuzzy rule interpolation using feature selection techniques

Diao, Ren; Jin, Shangzhu; Shen, Qiang

doi:10.1109/fuzz-ieee.2014.6891818

Cited by 17 publications

(7 citation statements)

References 29 publications

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“…Whilst the FRI literature has seen many methods (e.g., [2]- [4]) being proposed, most of which share a common assumption that the rule antecedents are of equal significance while performing rule interpolation. A recent focus of developing FRI techniques is to relax this assumption, by introducing weights to the individual antecedent attributes, such as [5], [6]. Nevertheless, these weighted FRI approaches require additional information for calculating the weights other than that contained within the sparse rule base.…”

Section: Introductionmentioning

confidence: 99%

Interpolation With Just Two Nearest Neighboring Weighted Fuzzy Rules

Shang

et al. 2020

IEEE Trans. Fuzzy Syst.

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Fuzzy rule interpolation (FRI) enables sparse fuzzy rule-based systems to derive an interpolated conclusion using neighbouring rules, when presented with an observation that matches none of the given rules. The efficacy of FRI has been further empowered by the recent development of weighted FRI techniques, particularly the one that introduces attribute weights of rule antecedents from the given rule base, removing the conventional assumption of antecedent attributes having equal weighting or significance. However, such work was carried out within the specific transformation-based FRI mechanism. This short paper reports the results of generalising it through enhancing two alternative representative FRI methods. The resultant weighted FRI algorithms facilitate the individual attribute weights to be integrated throughout the corresponding procedures of the conventional unweighted methods. With systematical comparative evaluations over benchmark classification problems, it is empirically demonstrated that these algorithms work effectively and efficiently using just two nearest neighbouring rules.

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Section: Introductionmentioning

confidence: 99%

Interpolation With Just Two Nearest Neighboring Weighted Fuzzy Rules

Shang

et al. 2020

IEEE Trans. Fuzzy Syst.

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“…This can lead to inaccurate or incorrect interpolated results. FRI methods that exploit rules with weighted antecedents have therefore, been introduced to remedy the adverse side-effects of this equal significance assumption [20]- [23]. For example, Genetic Algorithms (GA) have been applied to learn the weights of rule antecedents in support of FRI [24], but this incurs a substantial increase in computation overheads and requires the setting of many additional GA parameters.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Rule Based Interpolative Reasoning Supported by Attribute Ranking

Shang

et al. 2018

IEEE Trans. Fuzzy Syst.

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Q. (2018). Fuzzy rule-based interpolative reasoning supported by attribute ranking. IEEE Transactions on Fuzzy Systems, (99). https://doi.This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see Abstract-Using fuzzy rule interpolation (FRI) interpolative reasoning can be effectively performed with a sparse rule base where a given system observation does not match any fuzzy rules. Whilst offering a potentially powerful inference mechanism, in the current literature, typical representation of fuzzy rules in FRI assumes that all attributes in the rules are of equal significance in deriving the consequents. This is a strong assumption in practical applications, thereby often leading to less accurate interpolated results. To address this challenging problem, this work employs feature selection (FS) techniques to adjudge the relative significance of individual attributes and therefore, to differentiate the contributions of the rule antecedents and their impact upon FRI. This is feasible because FS provides a readily adaptable mechanism for evaluating and ranking attributes, being capable of selecting more informative features. Without requiring any acquisition of real observations, based on the originally given sparse rule base, the individual scores are computed using a set of training samples that are artificially created from the rule base through an innovative reverse engineering procedure. The attribute scores are integrated within the popular scale and move transformation-based FRI algorithm (while other FRI approaches may be similarly extended following the same idea), forming a novel method for attribute ranking-supported fuzzy interpolative reasoning. The efficacy and robustness of the proposed approach is verified through systematic experimental examinations in comparison with the original FRI technique, over a range of benchmark classification problems while utilising different FS methods. A specific and important outcome is that supported by attribute ranking, only two (i.e., the least number of) nearest adjacent rules are required to perform accurate interpolative reasoning, avoiding the need of searching for and computing with multiple rules beyond the immediate neighbourhood of a given observation.

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“…Choosing the rules for the rule base should be based on valid expert knowledge or empirical evidence based on the proposed system to maintain system integrity and validity. Tuning the inference engine by adding or removing rules, maintaining monotonicity property, and evaluating all possible rule conditions [36] from the experts also need to be considered to get the best output desired.…”

Section: Discussionmentioning

confidence: 99%

Student Assessment Based on Affective Factors in English Learning Using Fuzzy Inference

Bachtiar

Cooper

Sulistyo

et al. 2016

IJAE

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Studies reveal the influence of affective factors, emotion, behavior, and attitude on student success in learning. While quantification of student performance in cognitive factors is straightforward; measuring student performance in affective factors is indirect. Affective factors are commonly represented in linguistic and imprecise terms. This study proposes a method to identify student performance based on student affective factors in learning English using Fuzzy Systems. The system is then empirically validated. In this study, affective factors are selected based on student performance in English learning. A questionnaire is developed to collect data on student affective factors. Fuzzy sets are built based on the factors obtained. A fuzzy inference system is built to infer student achievement in English learning based on student affective factors. The result showed an empirically valid model that is useful as a basis to understand student performance in affective factors and to assess students as an additional evaluation.

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Antecedent selection in fuzzy rule interpolation using feature selection techniques

Cited by 17 publications

References 29 publications

Interpolation With Just Two Nearest Neighboring Weighted Fuzzy Rules

Interpolation With Just Two Nearest Neighboring Weighted Fuzzy Rules

Fuzzy Rule Based Interpolative Reasoning Supported by Attribute Ranking

Student Assessment Based on Affective Factors in English Learning Using Fuzzy Inference

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