How Fuzzy Concepts Contribute to Machine Learning最新文献

A Hybrid Filter-Based Feature Selection Method via Hesitant Fuzzy and Rough Sets Concepts 基于犹豫模糊和粗糙集概念的混合滤波特征选择方法

How Fuzzy Concepts Contribute to Machine Learning Pub Date : 2018-10-10 DOI: 10.22111/ijfs.2018.4140

Mohammad Mohtashami, M. Eftekhari

{"title":"A Hybrid Filter-Based Feature Selection Method via Hesitant Fuzzy and Rough Sets Concepts","authors":"Mohammad Mohtashami, M. Eftekhari","doi":"10.22111/ijfs.2018.4140","DOIUrl":"https://doi.org/10.22111/ijfs.2018.4140","url":null,"abstract":"High dimensional microarray datasets are difficult to classify since they have many features with small number ofinstances and imbalanced distribution of classes. This paper proposes a filter-based feature selection method to improvethe classification performance of microarray datasets by selecting the significant features. Combining the concepts ofrough sets, weighted rough set, fuzzy rough set and hesitant fuzzy sets for developing an effective algorithm is the maincontribution of this paper. The mentioned method has two steps, in the first step, four discretization approaches areapplied to discretize continuous datasets and selects a primary subset of features by combining of weighted rough setdependency degree and information gain via hesitant fuzzy aggregation approach. In the second step, a significancemeasure of features (defined by fuzzy rough concepts) is employed to remove redundant features from primary set.The Wilcoxon Signed Ranked tes (A Non-parametric statistical test) is conducted for comparing the presented methodwith ten feature selection methods across seven datasets. The results of experiments show that the proposed methodis able to select a significant subset of features and it is an effective method in the literature in terms of classificationperformance and simplicity.","PeriodicalId":212493,"journal":{"name":"How Fuzzy Concepts Contribute to Machine Learning","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131854976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Comparing Different Stopping Criteria for Fuzzy Decision Tree Induction Through IDFID3 通过IDFID3比较模糊决策树归纳的不同停止准则

How Fuzzy Concepts Contribute to Machine Learning Pub Date : 2014-02-25 DOI: 10.22111/IJFS.2014.1394

M. Zeinalkhani, M. Eftekhari

{"title":"Comparing Different Stopping Criteria for Fuzzy Decision Tree Induction Through IDFID3","authors":"M. Zeinalkhani, M. Eftekhari","doi":"10.22111/IJFS.2014.1394","DOIUrl":"https://doi.org/10.22111/IJFS.2014.1394","url":null,"abstract":"Fuzzy Decision Tree (FDT) classifiers combine decision trees with approximate reasoning offered by fuzzy representation to deal with language and measurement uncertainties. When a FDT induction algorithm utilizes stopping criteria for early stopping of the tree's growth, threshold values of stopping criteria will control the number of nodes. Finding a proper threshold value for a stopping criterion is one of the greatest challenges to be faced in FDT induction. In this paper, we propose a new method named Iterative Deepening Fuzzy ID3 (IDFID3) for FDT induction that has the ability of controlling the tree’s growth via dynamically setting the threshold value of stopping criterion in an iterative procedure. The final FDT induced by IDFID3 and the one obtained by common FID3 are the same when the numbers of nodes of induced FDTs are equal, but our main intention for introducing IDFID3 is the comparison of different stopping criteria through this algorithm. Therefore, a new stopping criterion named Normalized Maximum fuzzy information Gain multiplied by Number of Instances (NMGNI) is proposed and IDFID3 is used for comparing it against the other stopping criteria. Generally speaking, this paper presents a method to compare different stopping criteria independent of their threshold values utilizing IDFID3. The comparison results show that FDTs induced by the proposed stopping criterion in most situations are superior to the others and number of instances stopping criterion performs better than fuzzy information gain stopping criterion in terms of complexity (i.e. number of nodes) and classification accuracy. Also, both tree depth and fuzzy information gain stopping criteria, outperform fuzzy entropy, accuracy and number of instances in terms of mean depth of generated FDTs.","PeriodicalId":212493,"journal":{"name":"How Fuzzy Concepts Contribute to Machine Learning","volume":"31 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134128153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Preliminaries 预赛

How Fuzzy Concepts Contribute to Machine Learning Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-94066-9_1

M. Eftekhari, Adel Mehrpooya, Farid Saberi-Movahed, Vicenç Torra

引用次数: 1

Unsupervised Feature Selection Method Based on Sensitivity and Correlation Concepts for Multiclass Problems 基于敏感性和相关性概念的多类问题无监督特征选择方法

How Fuzzy Concepts Contribute to Machine Learning Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-94066-9_3

M. Eftekhari, Adel Mehrpooya, Farid Saberi-Movahed, V. Torra

引用次数: 0

Fuzzy Partitioning of Continuous Attributes Through Crisp Discretization 基于离散化的连续属性模糊划分

How Fuzzy Concepts Contribute to Machine Learning Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-94066-9_4

M. Eftekhari, Adel Mehrpooya, Farid Saberi-Movahed, V. Torra

引用次数: 1

Ensemble of Feature Selection Methods: A Hesitant Fuzzy Set Based Approach 特征选择方法集成:一种基于犹豫模糊集的方法

How Fuzzy Concepts Contribute to Machine Learning Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-94066-9_8

M. Eftekhari, Adel Mehrpooya, Farid Saberi-Movahed, V. Torra

引用次数: 0

Distributed Feature Selection: A Hesitant Fuzzy Correlation Concept for High-Dimensional Microarray Datasets 分布式特征选择:高维微阵列数据集的犹豫模糊关联概念

How Fuzzy Concepts Contribute to Machine Learning Pub Date : 1900-01-01 DOI: 10.1007/978-3-030-94066-9_9

M. Eftekhari, Adel Mehrpooya, Farid Saberi-Movahed, V. Torra

引用次数: 0