Supervised feature subset selection with ordinal optimization

Feng, Dingcheng; Chen, Feng; Xu, Wenli

doi:10.1016/j.knosys.2013.11.004

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…The goal of feature selection [7][8][9][10][11] is to select the smallest subset of original features which maintains some meaningful characteristics with respect to a chosen criterion. According to the possible use of output information (e.g.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised feature selection based on the Morisita estimator of intrinsic dimension

Golay

Kanevski

2017

Knowledge-Based Systems

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This paper deals with a new filter algorithm for selecting the smallest subset of features carrying all the information content of a data set (i.e. for removing redundant features). It is an advanced version of the fractal dimension reduction technique, and it relies on the recently introduced Morisita estimator of Intrinsic Dimension (ID). Here, the ID is used to quantify dependencies between subsets of features, which allows the effective processing of highly non-linear data. The proposed algorithm is successfully tested on simulated and real world case studies. Different levels of sample size and noise are examined along with the variability of the results. In addition, a comprehensive procedure based on random forests shows that the data dimensionality is significantly reduced by the algorithm without loss of relevant information. And finally, comparisons with benchmark feature selection techniques demonstrate the promising performance of this new filter.

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Section: Introductionmentioning

confidence: 99%

Unsupervised feature selection based on the Morisita estimator of intrinsic dimension

Golay

Kanevski

2017

Knowledge-Based Systems

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“…Among existing feature selection algorithms, supervised feature selection algorithms are commonly employed to process the data with class labels, in which there are some representatives, such as feature selection algorithm with feature selection algorithm based on mRMR [32], sparsity-inducing norms [14], feature selection algorithm based on t-test [44,45], feature subset selection algorithm with ordinal optimization [5] and feature selection algorithm based on neighborhood multi-granulation fusion [25]. For the investigation of feature selection, one of critical issues is how to select feature subset, and filters, wrappers and embedded methods have been generally recognized as the most popular methods to solve the issue [2,8].…”

Section: Introductionmentioning

confidence: 99%

Compacted decision tables based attribute reduction

Wei

Wang

Liang

et al. 2015

Knowledge-Based Systems

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“…Related studies of feature selection and data reduction have shown some promising results that the performance of prediction models with one of the data preprocessing step is better than the one without data preprocessing (Feng et al, 2014;Gunal and Edizkan, 2008;Leyva et al, 2014;Orsenigo and Vercellis, 2013;Piramuthu, 2004;Tsai, 2009;Tsai and Chang, 2013;Wang and Chiang, 2008). However, they only focus on either selecting more representative features or reducing faulty data for better classification or prediction.…”

Section: Introductionmentioning

confidence: 99%

Dimensionality and data reduction in telecom churn prediction

Lin

Tsai

2014

Kybernetes

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Purpose -Churn prediction is a very important task for successful customer relationship management. In general, churn prediction can be achieved by many data mining techniques. However, during data mining, dimensionality reduction (or feature selection) and data reduction are the two important data preprocessing steps. In particular, the aims of feature selection and data reduction are to filter out irrelevant features and noisy data samples, respectively. The purpose of this paper, performing these data preprocessing tasks, is to make the mining algorithm produce good quality mining results. Design/methodology/approach -Based on a real telecom customer churn data set, seven different preprocessed data sets based on performing feature selection and data reduction by different priorities are used to train the artificial neural network as the churn prediction model. Findings -The results show that performing data reduction first by self-organizing maps and feature selection second by principal component analysis can allow the prediction model to provide the highest prediction accuracy. In addition, this priority allows the prediction model for more efficient learning since 66 and 62 percent of the original features and data samples are reduced, respectively. Originality/value -The contribution of this paper is to understand the better procedure of performing the two important data preprocessing steps for telecom churn prediction.

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Supervised feature subset selection with ordinal optimization

Cited by 11 publications

References 18 publications

Unsupervised feature selection based on the Morisita estimator of intrinsic dimension

Unsupervised feature selection based on the Morisita estimator of intrinsic dimension

Compacted decision tables based attribute reduction

Dimensionality and data reduction in telecom churn prediction

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