Feature Extraction Using Clustering of Protein

Bonet, Isis; Saeys, Yvan; Abalo, Ricardo Grau; García, María M.; Sanchez, Robersy; Peer, Yves Van de

doi:10.1007/11892755_64

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…The feature extraction context, which is the one most closely related to this study, can be further divided into two sub-categories: feature clustering and data clustering. The feature clustering approach is extensively used, e.g., to facilitate face recognition [7] or improve classification of proteins by clustering the amino acids which they are composed of [2]. This approach is similar to ours in a sense that it also relies on clustering and also generates features based on the representatives of the obtained clusters.…”

Section: Related Workmentioning

confidence: 99%

A study on using data clustering for feature extraction to improve the quality of classification

Piernik

Morzy

2021

Knowl Inf Syst

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There is a certain belief among data science researchers and enthusiasts alike that clustering can be used to improve classification quality. Insofar as this belief is fairly uncontroversial, it is also very general and therefore produces a lot of confusion around the subject. There are many ways of using clustering in classification and it obviously cannot always improve the quality of predictions, so a question arises, in which scenarios exactly does it help? Since we were unable to find a rigorous study addressing this question, in this paper, we try to shed some light on the concept of using clustering for classification. To do so, we first put forward a framework for incorporating clustering as a method of feature extraction for classification. The framework is generic w.r.t. similarity measures, clustering algorithms, classifiers, and datasets and serves as a platform to answer ten essential questions regarding the studied subject. Each answer is formulated based on a separate experiment on 16 publicly available datasets, followed by an appropriate statistical analysis. After performing the experiments and analyzing the results separately, we discuss them from a global perspective and form general conclusions regarding using clustering as feature extraction for classification.

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Section: Related Workmentioning

confidence: 99%

A study on using data clustering for feature extraction to improve the quality of classification

Piernik

Morzy

2021

Knowl Inf Syst

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“…Once groups of similar features are formed, a linear transformation is realized to extract a new set of features. Actually, there exists another work that exploit clustering algorithm as a feature extraction technique to find new features [9]. It focuses on the prediction of HIV protease resistance to drugs.…”

Section: Clustering Based Feature Extraction Algorithmmentioning

confidence: 99%

A New Feature Extraction Method Based on Clustering for Face Recognition

Ferchichi¹,

Zidi

Laabidi³

et al. 2011

Engineering Applications of Neural Networks

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Abstract. When solving a pattern classification problem, it is common to apply a feature extraction method as a pre-processing step, not only to reduce the computation complexity but also to obtain better classification performance by reducing the amount of irrelevant and redundant information in the data. In this study, we investigate a novel schema for linear feature extraction in classification problems. The method we have proposed is based on clustering technique to realize feature extraction. It focuses in identifying and transforming redundant information in the data. A new similarity measure-based trend analysis is devised to identify those features. The simulation results on face recognition show that the proposed method gives better or competitive results when compared to conventional unsupervised methods like PCA and ICA.

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“…Recently, the use of clustering has been investigated for the extraction of features. The applicability of this approach was proven in the case of text classification problems [17], [1] and protein sequences analysis [4]. For each application domain, a specific functional similarity measures was determined.…”

Section: Introductionmentioning

confidence: 99%

Feature Clustering based MIM for a New Feature Extraction Method

Ferchichi

Zidi

Maouche

et al. 2013

INT J COMPUT COMMUN

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In this paper, a new unsupervised Feature Extraction appoach is presented, which is based on feature clustering algorithm. Applying a divisive clustering algorithm, the method search for a compression of the information contained in the original set of features. It investigates the use of Mutual Information Maximization (MIM) to find appropriate transformation of clusterde features. Experiments on UCI datasets show that the proposed method often outperforms conventional unsupervised methods PCA and ICA from the point of view of classification accuracy.

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Feature Extraction Using Clustering of Protein

Cited by 4 publications

References 10 publications

A study on using data clustering for feature extraction to improve the quality of classification

A study on using data clustering for feature extraction to improve the quality of classification

A New Feature Extraction Method Based on Clustering for Face Recognition

Feature Clustering based MIM for a New Feature Extraction Method

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