Journal ArticleDOI
Soft computing for feature analysis
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TLDR
This work focuses only on one aspect of pattern recognition, feature analysis, and discusses various methods using fuzzy logic, neural networks and genetic algorithms for feature ranking, selection and extraction including structure preserving dimensionality reduction.About:
This article is published in Fuzzy Sets and Systems.The article was published on 1999-04-16. It has received 66 citations till now. The article focuses on the topics: Feature (machine learning) & Soft computing.read more
Citations
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Journal ArticleDOI
Designing fuzzy inference systems from data: An interpretability-oriented review
TL;DR: The paper analyzes the main methods for automatic rule generation and structure optimization and grouped them into several families and compared according to the rule interpretability criterion.
Journal ArticleDOI
Data analysis for electronic nose systems
TL;DR: This review covers aspects of analysis from data normalisation methods to pattern recognition and classification techniques, and focuses on the use of artificial intelligence techniques such as neural networks and fuzzy logic for classification and genetic algorithms for feature (sensor) selection.
Journal ArticleDOI
Feature selection with neural networks
TL;DR: The algorithm developed outperformed the other methods by achieving higher classification accuracy on all the problems tested and compared the approach with five other feature selection methods, each of which banks on a different concept.
Journal ArticleDOI
Genetic programming for simultaneous feature selection and classifier design
TL;DR: This paper presents an online feature selection algorithm using genetic programming (GP) that simultaneously selects a good subset of features and constructs a classifier using the selected features and produces a feature ranking scheme.
Journal ArticleDOI
Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling
Shang-Ming Zhou,John Q. Gan +1 more
TL;DR: In order to clearly discriminate the different roles of fuzzy sets, input variables, and other components in achieving an interpretable fuzzy model, a taxonomy of fuzzy model interpretability is first proposed in terms of low- level interpretability and high-level interpretability in this paper.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Book
Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.