Comparing the dimensionality reduction methods in gene expression databases

Borges, Helyane Bronoski; Nievola, Júlio César

doi:10.1016/j.eswa.2012.03.015

Cited by 15 publications

(13 citation statements)

References 12 publications

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“…It is important to note that the attribute selection results presented, concerning the gene expression domain by attribute selection, are from the research of Borges and Nievola (2012). The three chosen databases were subjected to the seven classifiers.…”

Section: Resultsmentioning

confidence: 99%

“…These sets have been used by Borges and Nievola (2012) in their studies. The three data sets were extracted from the Kent Ridge Bio-medical Dataset Repository.…”

Section: Discussionmentioning

confidence: 99%

“…These sets were already used by Borges and Nievola (2012) in their studies. Table 1 presents the characteristics of these databases.…”

Section: Description Of the Data Setsmentioning

confidence: 99%

“…In the filter approach, two algorithms were used: correlation-based feature selection (Hall, 1999) and consistency subset evaluator (Tan et al, 2008). For the wrapper approach, the classification algorithms used were: naïve Bayes, J48, support vector machines (SVM) and k-NN (for k = 1, k = 3, k = 5, and k = 7) (Borges and Nievola, 2012). The method was used through an algorithm for the identification of the most applicable attributes, and after the generation of a new database created from framework concepts, the Weka environment was used.…”

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confidence: 99%

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Proposed method for dimensionality reduction based on framework in gene expression domain

Macedo¹,

Ishikawa²,

Santos³

et al. 2014

Genet. Mol. Res.

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ABSTRACT. The excessive use of attributes may affect the search for patterns and extraction of useful knowledge, because they harm the learning performance of algorithms in both speed and success rate. The use of dimensionality reduction methods is therefore an important alternative; however, these methods do not deal with the reduction of attributes in a specific area. This article presents a method based on framework concepts of domain for reducing attributes in a domain. The input method is a set of databases related to a domain, and the main process is the identification of common and variable attributes, plus the reduction of attributes in the original database. The proposed method was applied in the gene expression domain, using databases. The method can be used to analyze the most relevant attributes in a specific domain, granting greater confidence for models created for the application of a data mining task, thus, a previously known method in data mining. Attribute selection was also applied in the three databases for the comparison of the results. Analyses of the results using the criterion of cross-validation revealed that the employment of the methods resulted in the improvement of success rates compared to the databases

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Section: Resultsmentioning

confidence: 99%

“…These sets have been used by Borges and Nievola (2012) in their studies. The three data sets were extracted from the Kent Ridge Bio-medical Dataset Repository.…”

Section: Discussionmentioning

confidence: 99%

“…These sets were already used by Borges and Nievola (2012) in their studies. Table 1 presents the characteristics of these databases.…”

Section: Description Of the Data Setsmentioning

confidence: 99%

mentioning

confidence: 99%

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Proposed method for dimensionality reduction based on framework in gene expression domain

Macedo¹,

Ishikawa²,

Santos³

et al. 2014

Genet. Mol. Res.

View full text Add to dashboard Cite

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“…Unsupervised methods try to keep the structure information of data in its low-dimension representation, for instance, of the two typical unsupervised methods, principal component analysis (PCA) attempts to discover the variance of the data [2] while locality preserving projection (LPP) aims at preserving the locality relation [3]. Supervised methods, such as linear discriminate analysis (LDA) [4] and maximum margin criterion (MMC) [5], make use of the labeled data to obtain the reduced low-dimensional features containing the most discriminative information. Basically, supervised methods outperform unsupervised ones when labeled data are suf cient.…”

Section: Introductionmentioning

confidence: 99%

Active constraints selection based semi-supervised dimensionality in ensemble subspaces

Zeng

Nie

Zhang

2015

J. of Syst. Eng. Electron.

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Semi-supervised dimensionality reduction (SSDR) has attracted an increasing amount of attention in this big-data era. Many algorithms have been developed with a small number of pairwise constraints to achieve performances comparable to those of fully supervised methods. However, one challenging problem with semi-supervised approaches is the appropriate choice of the constraint set, including the cardinality and the composition of the constraint set, which to a large extent, affects the performance of the resulting algorithm. In this work, we address the problem by incorporating ensemble subspace and active learning into dimensionality reduction and propose a new algorithm, termed as global and local scatter based SSDR with active pairwise constraints selection in ensemble subspaces (SSGL-ESA). Unlike traditional methods that select the supervised information in one subspace, we pick up pairwise constraints in ensemble subspace, where a novel active learning algorithm is designed with both exploration and ltering to generate informative pairwise constraints. The automatic constraint selection approach proposed in this paper can be generalized to be used with all constraint-based semi-supervised learning algorithms. Comparative experiments are conducted on two face database and the results validate the effectiveness of the proposed method.

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