A two-stage gene selection scheme utilizing MRMR filter and GA wrapper

Akadi, Ali El; Amine, Aouatif; Ouardighi, A. El; Aboutajdine, Driss

doi:10.1007/s10115-010-0288-x

Cited by 148 publications

(60 citation statements)

References 36 publications

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“…Therefore, recently two-stage models have been proposed by various researchers [21,22,23,24,25]. [28], for a detailed description of mathematical process of PCA).…”

Section: Literature Surveymentioning

confidence: 99%

Survey Paper on Feature Extraction Methods in Text Categorization

Saxena¹,

Saritha²,

Prasad³

2017

IJCA

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As the world is moving towards globalization, digitization of text has been escalating a lot and the need to organize, categorize and classify text has become obligatory. Disorganization or little categorization and sorting of text may result in dawdling response time of information retrieval. There has been the 'curse of dimensionality' (as termed by Bellman)[1] problem, namely the inherent sparsity of high dimensional spaces. Thus, the search for a possible presence of some unspecified structure in such a high dimensional space can be difficult. This is the task of feature reduction methods. They obtain the most relevant information from the original data and represent the information in a lower dimensionality space.In this paper, all the applied methods on feature extraction on text categorization from the traditional bag-of-words model approach to the unconventional neural networks are discussed.

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“…Therefore, recently two-stage models have been proposed by various researchers [21,22,23,24,25]. [28], for a detailed description of mathematical process of PCA).…”

Section: Literature Surveymentioning

confidence: 99%

Survey Paper on Feature Extraction Methods in Text Categorization

Saxena¹,

Saritha²,

Prasad³

2017

IJCA

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“…The wrapper approach is used for feature selection algorithms (in this case, PSO, GA and RS) because wrapper methods apply an induction algorithm (in this case, ANN) to evaluate the merit of selected feature. The rationale of wrapper methods is that the induction algorithm (ANN) provides a better accuracy than using separate data measures or characteristics (El Akadi et al 2011). Wrapper methods usually achieve better results than filters because they are adjusted to particular interaction between training data and the induction algorithm.…”

Section: Feature Selection For Dengue Datamentioning

confidence: 99%

Feature Selection Algorithms for Malaysian Dengue Outbreak Detection Model

Husam¹,

Abuhamad²,

Bakar³

et al. 2017

JSM

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“…According to their findings, the choice of feature selection methods and the number of genes substantially influence classification success. In [17], a Minimum RedundancyMaximum Relevance (MRMR) filter is used in combination with a Genetic Algorithm. Shah et al [53] introduce a formulation that includes the task of feature selection as well as classification.…”

Section: Feature Selectionmentioning

confidence: 99%

Improving pattern classification of DNA microarray data by using PCA and logistic regression

Ocampo-Vega

Sánchez-Ante

Luna

et al. 2016

IDA

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Abstract. DNA microarrays is a technology that can be used to diagnose cancer and other diseases. To automate the analysis of such data, pattern recognition and machine learning algorithms can be applied. However, the curse of dimensionality is unavoidable: very few samples to train, and many attributes in each sample. As the predictive accuracy of supervised classifiers decays with irrelevant and redundant features, the necessity of a dimensionality reduction process is essential. The main idea is to retain only the genes that are the most influential in the classification of the disease. In this paper, a new methodology based on Principal Component Analysis and Logistics Regression is proposed. Our method enables the selection of particular genes that are relevant for classification. Experiments were run using eight different classifiers on two benchmark datasets: Leukemia and Lymphoma. The results show that our method not only reduces the number of required attributes, but also increase the classification accuracy in more than 10% in all the cases we tested.

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A two-stage gene selection scheme utilizing MRMR filter and GA wrapper

Cited by 148 publications

References 36 publications

Survey Paper on Feature Extraction Methods in Text Categorization

Survey Paper on Feature Extraction Methods in Text Categorization

Feature Selection Algorithms for Malaysian Dengue Outbreak Detection Model

Improving pattern classification of DNA microarray data by using PCA and logistic regression

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