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Titlebook: How Fuzzy Concepts Contribute to Machine Learning; Mahdi Eftekhari,Adel Mehrpooya,Vicen? Torra Book 2022 The Editor(s) (if applicable) and

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發(fā)表于 2025-3-21 17:52:15 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱How Fuzzy Concepts Contribute to Machine Learning
編輯Mahdi Eftekhari,Adel Mehrpooya,Vicen? Torra
視頻videohttp://file.papertrans.cn/429/428612/428612.mp4
概述Recent research on the application of fuzzy and hesitant fuzzy sets in machine learning tasks.Shows how fuzzy concepts can be used to solve multi-criteria decision making challenges raised in machine
叢書(shū)名稱Studies in Fuzziness and Soft Computing
圖書(shū)封面Titlebook: How Fuzzy Concepts Contribute to Machine Learning; Mahdi Eftekhari,Adel Mehrpooya,Vicen? Torra Book 2022 The Editor(s) (if applicable) and
描述This book introduces some?contemporary?approaches?on the application of?fuzzy and hesitant fuzzy sets in machine learning tasks such as?classification, clustering and dimension?reduction.?Many?situations?arise?in machine learning algorithms?in?which?applying methods for uncertainty?modeling and?multi-criteria?decision making can lead to?a?better?understanding of?algorithms behavior as well as achieving?good performances.?Specifically,?the present book is a collection of novel viewpoints?on how?fuzzy and?hesitant fuzzy concepts?can be?applied?to?data uncertainty modeling as?well as?being used to solve?multi-criteria decision?making challenges?raised in machine learning problems. Using the multi-criteria decision?making framework, the book shows how different algorithms, rather than?human experts,?are?employed?to determine membership degrees. The book is expected to bring closer?the? communities of pure mathematicians of fuzzy?sets and data scientists.?.
出版日期Book 2022
關(guān)鍵詞Fuzzy Concepts; Machine Learning; Fuzzy Sets; Hesitant Fuzzy Sets; Data Uncertainty Modeling
版次1
doihttps://doi.org/10.1007/978-3-030-94066-9
isbn_softcover978-3-030-94068-3
isbn_ebook978-3-030-94066-9Series ISSN 1434-9922 Series E-ISSN 1860-0808
issn_series 1434-9922
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Unsupervised Feature Selection Method Based on Sensitivity and Correlation Concepts for Multiclass Pive clustering and the concepts of sensitivity and Pearson’s correlation. We show how this method is employed as the fitness function in a genetic algorithm (GA) in order to evaluate feature subsets. Informally, the method works as follows. First, the sensitivity index of each feature is computed by
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Hesitant Fuzzy Decision Tree Approach for Highly Imbalanced Data Classificationd data when the distribution of data samples is not the same in different classes. That is, there is usually a large difference among the number of instances in different classes. If this is the case, learning algorithms, with their goal of maximizing the accuracy of the inferred model, may ignore t
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Ensemble of Feature Selection Methods: A Hesitant Fuzzy Set Based Approach significantly smaller than the number of instances, this is not the case for DNA microarray data. This chapter introduces a feature selection algorithm based on a greedy search, and it uses main concepts from hesitant fuzzy set theory as an heuristic to tackle the feature selection problem for high
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A Hybrid Filter-Based Feature Selection Method via Hesitant Fuzzy and Rough Sets Conceptsthe significant features. In particular, the approach described in this chapter is based on the combination of concepts related to rough set theory to build a feature selection algorithm. The concepts considered include weighted rough sets, fuzzy rough sets, and hesitant fuzzy sets.
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