Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement

By Yaochu Jin. IEEE transactions on fuzzy systems, 2000

507 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/248aa5cb065deb2984d1ef64b8d3afe493db1d75

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Citations in the corpus

Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, background): “In these situations it is very useful to have an intelligent system capable of selecting the most relevant features needed to build an accurate and reliable model for the process [170,284,322].”
Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling (2008, unknown): “Hence, model interpretability improvement is regarded as one of the most important issues in data-driven fuzzy modelling [5,25,46,83,91,94,93,113,156,174,179,190].”
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies (2008, methodology): “This is also true if we look at the definition of interpretability of fuzzy systems [6], [41].”
Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining (2004, background): “While the main objective in the design of fuzzy rule-based systems has been the performance maximization, their comprehensibility has also been taken into account in some recent studies [3,4,19,24,25,27,28].”
Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “The method in [281] is based on a five-step algorithm.”
Ten years of genetic fuzzy systems: current framework and new trends (2001, background): “In [79], an interesting scheme is suggested for linguistic FRBSs, including the variable selection within a more complex derivation process (rule generation, DB tuning, and rule selection).”
Perspectives of fuzzy systems and control (2005, unknown): “The linguistic interpretability and transparency of fuzzy models constructed from data therefore became important research items in the literature [45,46,15,43,29].”
A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, unknown): “Instead, there are many different issues which must be taken into account in order to obtain a human interpretable structure (such as, for example, the RB compactness or the semantic comprehensibility of the fuzzy partitions, as we will see as follows) [81, 101, 102, 118, 148].”
Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning (2007, methodology): “Some researchers tried to simultaneously perform the accuracy maximization and the complexity minimization in order to design fuzzy rule-based systems with high accuracy and high interpretability [2] [3] [4].”