A New Feature Selection Scheme Using a Data Distribution Factor for Unsupervised Nominal Data

No order correlation or similarity metric exists in nominal data, and there will always be more redundancy in a nominal dataset, which means that an efficient mutual information-based nominal-data feature selection method is relatively difficult to find. In this paper, a nominal-data feature selection method based on mutual information without data transformation, called the redundancy-removing more relevance less redundancy algorithm, is proposed. By forming several new information-related definitions and the corresponding computational methods, the proposed method can compute the information-related amount of nominal data directly. Furthermore, by creating a new evaluation function that considers both the relevance and the redundancy globally, the new feature selection method can evaluate the importance of each nominal-data feature. Although the presented feature selection method takes commonly used MIFS-like forms, it is capable of handling high-dimensional datasets without expensive computations. We perform extensive experimental comparisons of the proposed algorithm and other methods using three benchmarking nominal datasets with two different classifiers. The experimental results demonstrate the average advantage of the presented algorithm over the well-known NMIFS algorithm in terms of the feature selection and classification accuracy, which indicates that the proposed method has a promising performance.

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A redundancy-removing feature selection algorithm for nominal data

10.7287/peerj.preprints.1184v1 ◽

2015 ◽

Cited By ~ 1

Author(s):

Zhihua Li

Keyword(s):

Feature Selection ◽

Mutual Information ◽

Feature Selection Method ◽

Selection Method ◽

Selection Algorithm ◽

Nominal Data ◽

New Information ◽

New Feature ◽

High Dimensional Datasets ◽

Experimental Comparisons

No order correlation or similarity metric exists in nominal data, and there will always be more redundancy in a nominal dataset, which means that an efficient mutual information-based nominal-data feature selection method is relatively difficult to find. In this paper, a nominal-data feature selection method based on mutual information without data transformation, called the redundancy-removing more relevance less redundancy algorithm, is proposed. By forming several new information-related definitions and the corresponding computational methods, the proposed method can compute the information-related amount of nominal data directly. Furthermore, by creating a new evaluation function that considers both the relevance and the redundancy globally, the new feature selection method can evaluate the importance of each nominal-data feature. Although the presented feature selection method takes commonly used MIFS-like forms, it is capable of handling high-dimensional datasets without expensive computations. We perform extensive experimental comparisons of the proposed algorithm and other methods using three benchmarking nominal datasets with two different classifiers. The experimental results demonstrate the average advantage of the presented algorithm over the well-known NMIFS algorithm in terms of the feature selection and classification accuracy, which indicates that the proposed method has a promising performance.

Download Full-text

A redundancy-removing feature selection algorithm for nominal data

10.7287/peerj.preprints.1184 ◽

2015 ◽

Author(s):

Zhihua Li

Keyword(s):

Feature Selection ◽

Mutual Information ◽

Feature Selection Method ◽

Selection Method ◽

Selection Algorithm ◽

Nominal Data ◽

New Information ◽

New Feature ◽

High Dimensional Datasets ◽

Experimental Comparisons

No order correlation or similarity metric exists in nominal data, and there will always be more redundancy in a nominal dataset, which means that an efficient mutual information-based nominal-data feature selection method is relatively difficult to find. In this paper, a nominal-data feature selection method based on mutual information without data transformation, called the redundancy-removing more relevance less redundancy algorithm, is proposed. By forming several new information-related definitions and the corresponding computational methods, the proposed method can compute the information-related amount of nominal data directly. Furthermore, by creating a new evaluation function that considers both the relevance and the redundancy globally, the new feature selection method can evaluate the importance of each nominal-data feature. Although the presented feature selection method takes commonly used MIFS-like forms, it is capable of handling high-dimensional datasets without expensive computations. We perform extensive experimental comparisons of the proposed algorithm and other methods using three benchmarking nominal datasets with two different classifiers. The experimental results demonstrate the average advantage of the presented algorithm over the well-known NMIFS algorithm in terms of the feature selection and classification accuracy, which indicates that the proposed method has a promising performance.

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A New Feature Selection Scheme for Emotion Recognition from Text

Applied Sciences ◽

10.3390/app10155351 ◽

2020 ◽

Vol 10 (15) ◽

pp. 5351

Author(s):

Zafer Erenel ◽

Oluwatayomi Rereloluwa Adegboye ◽

Huseyin Kusetogullari

Keyword(s):

Feature Selection ◽

Emotion Recognition ◽

Bag Of Words ◽

Document Representation ◽

Chi Square ◽

Selection Scheme ◽

Term Selection ◽

Selection Measures ◽

New Feature ◽

Better Than

This paper presents a new scheme for term selection in the field of emotion recognition from text. The proposed framework is based on utilizing moderately frequent terms during term selection. More specifically, all terms are evaluated by considering their relevance scores, based on the idea that moderately frequent terms may carry valuable information for discrimination as well. The proposed feature selection scheme performs better than conventional filter-based feature selection measures Chi-Square and Gini-Text in numerous cases. The bag-of-words approach is used to construct the vectors for document representation where each selected term is assigned the weight 1 if it exists or assigned the weight 0 if it does not exist in the document. The proposed scheme includes the terms that are not selected by Chi-Square and Gini-Text. Experiments conducted on a benchmark dataset show that moderately frequent terms boost the representation power of the term subsets as noticeable improvements are observed in terms of Accuracies.

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A lazy feature selection method for multi-label classification

Intelligent Data Analysis ◽

10.3233/ida-194878 ◽

2021 ◽

Vol 25 (1) ◽

pp. 21-34

Author(s):

Rafael B. Pereira ◽

Alexandre Plastino ◽

Bianca Zadrozny ◽

Luiz H.C. Merschmann

Keyword(s):

Feature Selection ◽

Text Categorization ◽

Feature Selection Method ◽

Selection Method ◽

Video Classification ◽

Classification Problems ◽

Class Label ◽

New Feature ◽

Feature Selection Techniques ◽

Biomolecular Analysis

In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This has led, in recent years, to a substantial amount of research in multi-label classification. More specifically, feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. This work presents a new feature selection method based on the lazy feature selection paradigm and specific for the multi-label context. Experimental results show that the proposed technique is competitive when compared to multi-label feature selection techniques currently used in the literature, and is clearly more scalable, in a scenario where there is an increasing amount of data.

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An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification

Sensors ◽

10.3390/s21113627 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3627

Author(s):

Bo Jin ◽

Chunling Fu ◽

Yong Jin ◽

Wei Yang ◽

Shengbin Li ◽

...

Keyword(s):

Feature Selection ◽

Local Structure ◽

Gene Selection ◽

Dimensional Space ◽

Original Data ◽

Global Structure ◽

Biological Data ◽

Special Treatment ◽

Selection Scheme ◽

Unsupervised Feature Selection

Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions. In recent years, unsupervised feature selection algorithms have attracted considerable attention in the field of gene selection as they can find the most discriminating subsets of genes, namely the potential information in biological data. Recent research also shows that maintaining the important structure of data is necessary for gene selection. However, most current feature selection methods merely capture the local structure of the original data while ignoring the importance of the global structure of the original data. We believe that the global structure and local structure of the original data are equally important, and so the selected genes should maintain the essential structure of the original data as far as possible. In this paper, we propose a new, adaptive, unsupervised feature selection scheme which not only reconstructs high-dimensional data into a low-dimensional space with the constraint of feature distance invariance but also employs ℓ2,1-norm to enable a matrix with the ability to perform gene selection embedding into the local manifold structure-learning framework. Moreover, an effective algorithm is developed to solve the optimization problem based on the proposed scheme. Comparative experiments with some classical schemes on real tumor datasets demonstrate the effectiveness of the proposed method.

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