Semi-supervised Feature Importance Evaluation with Ensemble Learning

Barkia, Hasna; Elghazel, Haytham; Aussem, Alexandre

doi:10.1109/icdm.2011.129

Cited by 14 publications

(5 citation statements)

References 24 publications

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“…For the production of distinct data sets, a blend of resampling and random subspace might be utilized. Several classifiers are trained, and then their output results are combined in ensemble learning methods [245].…”

Section: ) Semi-supervised Wrapper Methodsmentioning

confidence: 99%

A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

et al. 2022

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Feature selection is employed to reduce feature dimensions and computational complexity by eliminating irrelevant and redundant features. A vast amount of increasing data and its processing generate many feature sets, that are reduced by the feature selection process to improve the performance in all sorts of classification, regression, clustering models. This research performs a detailed analysis of motivation and concentrates on the fundamental architecture of feature selection. The study aims to establish a structured formation related to popular methods such as filter, wrapper, embedded into search strategies, evaluation criteria, and learning methods. Different methods organize a comparison of benefits and drawbacks followed by multiple classification algorithms and standard validation measures. The diversity of applications in multiple domains such as data retrieval, prediction analysis, and medical, intrusion, and industrial applications are efficiently highlighted. The study focused on some additional feature selection methods for handling big data. Nonetheless, new challenges have surfaced in the analysis of such data, which are also addressed in this study. Reflecting on commonly encountered challenges and clarifying how to obtain the absolute feature selection method are the significant components of this study.

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Section: ) Semi-supervised Wrapper Methodsmentioning

confidence: 99%

A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

et al. 2022

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“…T represents the redundancy threshold, and our goal is to select the top 20% of non-redundant augmented features, which start as an empty set. So, by the end of step 5, after 6 iterations, the features selected by RSSFS, i.e., ssl_set, and the supervised set, i.e., sl_set, are mentioned or selected, and their column indices are as follows: ssl_set=[ [1,3,7,11,13,21], [1,3,10,12,20,22], [1,3,9,11,23,25], [1,3,7,19,17,30], [1,3,7,11,20,29], [1,5,7,11,23,30] ] sl_set= [[3.7,11,17,21,28], [3.7,11,17,21,28], [3.7,11,17,23,19], [3.7,11,18,25,29], [3.7,9,16,27,22], [3.6,10,12,24,26]] freq_ssl_set= [1,3,7,11,23,30] freq_sl_set= [3.7,11,17,21,28] F...…”

Section: Case Studymentioning

confidence: 99%

“…So, by the end of step 5, after 6 iterations, the features selected by RSSFS, i.e., ssl_set, and the supervised set, i.e., sl_set, are mentioned or selected, and their column indices are as follows: ssl_set=[ [1,3,7,11,13,21], [1,3,10,12,20,22], [1,3,9,11,23,25], [1,3,7,19,17,30], [1,3,7,11,20,29], [1,5,7,11,23,30] ] sl_set= [[3.7,11,17,21,28], [3.7,11,17,21,28], [3.7,11,17,23,19], [3.7,11,18,25,29], [3.7,9,16,27,22], [3.6,10,12,24,26]] freq_ssl_set= [1,3,7,11,23,30] freq_sl_set= [3.7,11,17,21,28] Find the similarity between the frequent feature sets of semi-supervised and supervised. similar features=…”

Section: Case Studymentioning

confidence: 99%

Semi-supervised feature selection using maximum mutual information and minimum correlated feature set retrieved by augmented learning

Das,

Goswami,

Chakrabarti

et al. 2024

Preprint

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Feature selection is a critical pre-processing step in machine learning. For supervised problems, class labels are used to identify important features. However, the tagging of the data is labor intensive and hence costly. Consequently, there is an abundance of unlabeled data and limited labeled data. Hence, semi-supervised learning is very pertinent. The problem of feature selection is equally relevant for semi-supervised learning. In this research work, a fresh semi-supervised method of feature selection is proposed. In the first step, gradient boosting classifier is used for labeling the unlabeled portion of the data and augment the training set. Repeated sampling is done from the unlabeled portion, to generate multiple augmented training sets. Top-k features are selected based on mutual information from each augmented training set. While selecting the features, it is ensured that the features are not redundant using a correlation coefficient. A voting-based approach is used to combine these multiple feature sets. The proposed method is compared with a) Supervised Feature Selection on the full dataset (Benchmark) and b) Supervised Feature Selection on the labeled portion. On comparing these three methods across 18 datasets, it was found that semisupervised feature selection outperforms the supervised model based on F1 scores by 2.78% and 2.63% in two different configurations. Also, the model outperforms the benchmark by 0.36% and 1.12%.

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“…SL semisupervised feature selection methods can be categorized into five classes: graph‐based semisupervised feature selection, an example of which can be found in Cheng, Deng, Fu, Wang, and Qin's () study, self‐training‐based semisupervised feature selection (Bellal, Elghazel, & Aussem, ), cotraining‐based semisupervised feature selection (Barkia, Elghazel, & Aussem, ), SVM‐based semisupervised feature selection (Ang, Haron, & Hamed, ), and other semisupervised feature selection methods. Majority of semisupervised feature selection methods construct a graph using the training samples that correspond to graph‐based semisupervised learning methods.…”

Section: Multilabel Feature Selectionmentioning

confidence: 99%

Multilabel feature selection: A comprehensive review and guiding experiments

Kashef

Nezamabadi‐pour

Nikpour

2018

WIREs Data Min & Knowl

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Feature selection has been an important issue in machine learning and data mining, and is unavoidable when confronting with high‐dimensional data. With the advent of multilabel (ML) datasets and their vast applications, feature selection methods have been developed for dimensionality reduction and improvement of the classification performance. In this work, we provide a comprehensive review of the existing multilabel feature selection (ML‐FS) methods, and categorize these methods based on different perspectives. As feature selection and data classification are closely related to each other, we provide a review on ML learning algorithms as well. Also, to facilitate research in this field, a section is provided for setup and benchmarking that presents evaluation measures, standard datasets, and existing software for ML data. At the end of this survey, we discuss some challenges and open problems in this field that can be pursued by researchers in future. WIREs Data Mining Knowl Discov 2018, 8:e1240. doi: 10.1002/widm.1240 This article is categorized under: Technologies > Data Preprocessing

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Semi-supervised Feature Importance Evaluation with Ensemble Learning

Cited by 14 publications

References 24 publications

A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

Semi-supervised feature selection using maximum mutual information and minimum correlated feature set retrieved by augmented learning

Multilabel feature selection: A comprehensive review and guiding experiments

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