Feature selection by optimizing a lower bound of conditional mutual information

Peng, Hanyang; Fan, Yong

doi:10.1016/j.ins.2017.08.036

Cited by 62 publications

(23 citation statements)

References 41 publications

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“…We can see that the model with feature selection performs better than the model without feature selection. This finding is in accordance with previous works who stated that feature selection not only can facilitate the model's forecasting performance, but also can reduce the storage requirement (Peng & Fan, 2017;Zhang, Song, & Gong, 2017;Costea, Ferrara, & Şerban, 2017). Among all of the experiments, the model with FRST reaches the best forecasting quality.…”

Section: The Practical Results and Statistical Examinationsupporting

confidence: 91%

Decision Making by Extracting Soft Information From CSR News Report

Lin

Hsu

2018

Technological and Economic Development of Economy

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Abstract. This study examines the impact of corporate social responsibility (CSR) news reports on corporate operating performance forecasting using a large database of publicly-listed electronics firms in Taiwan. Applying text mining techniques and latent topic modelling, we construct and measure the intensity of the CSR-corpus index (ICSRI), which can compress tremendous amounts of CSR textual information content into synthesized meaningful dimensions. By doing so, we are able to break down CSR news reports into multiple dimensions and then examine which dimension(s) affects operating performance. To offer decision-makers with a comprehensive, overarching view of the corporate's operations, this study incorporates balanced scorecards (BSC) and multiple criteria decision analysis (MCDA) to form a final performance rank. The proposed approach, supported by real samples, can assist both internal and external stakeholders in allocating scarce resources to specific CSR dimensions to enhance a corporate's growth potential as well as to achieve a win-win situation.

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Section: The Practical Results and Statistical Examinationsupporting

confidence: 91%

Decision Making by Extracting Soft Information From CSR News Report

Lin

Hsu

2018

Technological and Economic Development of Economy

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“…Particularly, some references in very high-dimensional problems such as cancer detection via gene expression data, must be mentioned. The work of [36] presents a theoretical and practical framework for feature selection based on a conditional mutual information criterion. [35,50] focus on the chemotherapy effectiveness problems solved by means of ranking (SVM-RFE) and fuzzy if-then rules, respectively.…”

Section: Introductionmentioning

confidence: 99%

Variable selection in classification for multivariate functional data

Blanquero

Carrizosa

Jiménez-Cordero

et al. 2019

Information Sciences

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When classification methods are applied to high-dimensional data, selecting a subset of the predictors may lead to an improvement in the predictive ability of the estimated model, in addition to reducing the model complexity. In Functional Data Analysis (FDA), i.e., when data are functions, selecting a subset of predictors corresponds to selecting a subset of individual time instants in the time interval in which the functional data are measured. In this paper, we address the problem of selecting the most informative time instants in multivariate functional data, a case much less studied than its single-variate counterpart. Our proposal allows one to use in a very simple way high-order information of the data, e.g. monotonicity or convexity by means of the functional data derivatives. The aforementioned problem is addressed with tools of Global Optimization in continuous variables: the time instants are selected to maximize the correlation between the class label and the Support Vector Machine score used for classification. The effectiveness of the proposal is shown in univariate and multivariate datasets.

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“…(1), instead of the conventional Frobenius loss functions (i.e., least square loss function), because the squared root of a loss function is more robust to outliers (Peng and Fan 2016, 2017a, 2017b). To optimize Eq.…”

Section: Methodsmentioning

confidence: 99%

A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis

2018

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To characterize associations between genetic and neuroimaging data, a variety of analytic methods have been proposed in neuroimaging genetic studies. These methods have achieved promising performance by taking into account inherent correlation in either the neuroimaging data or the genetic data alone. In this study, we propose a novel robust reduced rank graph regression based method in a linear regression framework by considering correlations inherent in neuroimaging data and genetic data jointly. Particularly, we model the association analysis problem in a reduced rank regression framework with the genetic data as a feature matrix and the neuroimaging data as a response matrix by jointly considering correlations among the neuroimaging data as well as correlations between the genetic data and the neuroimaging data. A new graph representation of genetic data is adopted to exploit their inherent correlations, in addition to robust loss functions for both the regression and the data representation tasks, and a square-root-operator applied to the robust loss functions for achieving adaptive sample weighting. The resulting optimization problem is solved using an iterative optimization method whose convergence has been theoretically proved. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset have demonstrated that our method could achieve competitive performance in terms of regression performance between brain structural measures and the Single Nucleotide Polymorphisms (SNPs), compared with state-of-the-art alternative methods.

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Feature selection by optimizing a lower bound of conditional mutual information

Cited by 62 publications

References 41 publications

Decision Making by Extracting Soft Information From CSR News Report

Decision Making by Extracting Soft Information From CSR News Report

Variable selection in classification for multivariate functional data

A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis

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