Feature selection based on quality of information

Liu, Jinghua; Lin, Yaojin; Lin, Menglei; Wu, Shunxiang; Zhang, Jia

doi:10.1016/j.neucom.2016.11.001

Cited by 63 publications

(51 citation statements)

References 35 publications

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“…Readers are referred to the Discussion Section for more details on parameter settings. We compared the proposed TCbGA algorithm to several state-of-the-art feature selection algorithms, including DEMOFS [34] , MOEA/D [35], MDisABC [36], W-QEISS [37], SB-ELM [38], HPSO-LS [39], MoDE [40], GASNCM [41], GCACO [42], GCNC [43], UFSACO [44], FFW-DGC [45], QIFS [46], FSFWISIW [47], BALO [48], MI-SC [49], VMBACO [50], HDBPSO [51] and bGWO [52]. Table 3 shows the mean and standard deviation of the classification accuracy obtained by applying our algorithm to each dataset 25 times and the performance of other algorithms reported in the literature.…”

Section: Experiments and Resultsmentioning

confidence: 99%

A tribe competition-based genetic algorithm for feature selection in pattern classification

Xia

2017

Applied Soft Computing

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Abstract-Feature selection has always been a critical step in pattern recognition, in which evolutionary algorithms, such as the genetic algorithm (GA), are most commonly used. However, the individual encoding scheme used in various GAs would either pose a bias on the solution or require a pre-specified number of features, and hence may lead to less accurate results. In this paper, a tribe competition-based genetic algorithm (TCbGA) is proposed for feature selection in pattern classification. The population of individuals is divided into multiple tribes, and the initialization and evolutionary operations are modified to ensure that the number of selected features in each tribe follows a Gaussian distribution. Thus each tribe focuses on exploring a specific part of the solution space. Meanwhile, tribe competition is introduced to the evolution process, which allows the winning tribes, which produce better individuals, to enlarge their sizes, i.e. having more individuals to search their parts of the solution space. This algorithm, therefore, avoids the bias on solutions and requirement of a pre-specified number of features. We have evaluated our algorithm against several state-of-the-art feature selection approaches on 20 benchmark datasets. Our results suggest that the proposed TCbGA algorithm can identify the optimal feature subset more effectively and produce more accurate pattern classification.

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Section: Experiments and Resultsmentioning

confidence: 99%

A tribe competition-based genetic algorithm for feature selection in pattern classification

Xia

2017

Applied Soft Computing

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“…Similarly, if households do longer dishwashing via dishwasher, their electricity consumption is also likely to increase. Several researchers believe that both duration and frequency of appliance usage play a role in electricity demand [9,23]. Furthermore, the influence extent of appliances usage on electricity consumption primarily depends on the electricity consumption characteristics of appliances.…”

Section: Appliance and Cooking-heating Methodsmentioning

confidence: 99%

“…Embedded methods perform feature selection and model establishment simultaneously via a specific algorithm that takes advantage of its own feature selection process [23]. One example in residential electricity analysis is the LASSO method applied by Huebner et al in [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression

Wang

et al. 2018

Energies

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This paper aims to identity the significant impact factors (IFs) of the residential electricity consumption level (RECL) and to better understand the influence mechanism of IFs on RECL. The analysis of influence mechanism is commonly through regression model where feature selection must first be performed to pick out non-redundant IFs that is highly correlated with RECL. In contrast to the existing studies, this study recognizes the problem that majority feature selection methods (e.g., step regression) are limited to the identification of linear relationships and proposes a novel wrapper feature selection (WFS) method to address this issue. The WFS is based on genetic algorithm (GA) and multinomial logistic regression (MLR). GA is a searching algorithm used to generate different feature subsets (FSs) that consist of several IFs. MLR is a modeling algorithm used to score these FSs. Further, maximal information coefficient (MIC) is utilized to verify the validity of WFS for selecting IFs. Finally, MLR based explanatory model is established to excavate the relationship between selected IFs and RECL. The results of Ireland dataset based case study show that WFS can identify the significant and non-redundant IFs that are linearly or nonlinearly related to RECL. The details about how selected IFs affect RECL are also provided via the explanatory model. Such research can provide useful guidance for a wide range of stakeholders including local governments, electric power companies, and individual households.

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“…Radovic et al (2017) approached this problem by introducing a penalty term for mutual information shared between selected variables. Liu et al (2016) assigned a measure of feature quality by comparing conditional information of a variable on an outcome conditioned upon k-nearest-neighbor variables. By utilizing information-based methods, in our research, there is no need to assume underlying distributions FIGURE 1 | Panel model representation of a mediation model with a single exposure X, single mediator M, and single response Y, and the associated coefficients used for linear structural equation modeling.…”

Section: Introductionmentioning

confidence: 99%

An Information-Based Approach for Mediation Analysis on High-Dimensional Metagenomic Data

et al. 2020

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The human microbiome plays a critical role in the development of gut-related illnesses such as inflammatory bowel disease and clinical pouchitis. A mediation model can be used to describe the interaction between host gene expression, the gut microbiome, and clinical/health situation (e.g., diseased or not, inflammation level) and may provide insights into underlying disease mechanisms. Current mediation regression methodology cannot adequately model high-dimensional exposures and mediators or mixed data types. Additionally, regression based mediation models require some assumptions for the model parameters, and the relationships are usually assumed to be linear and additive. With the microbiome being the mediators, these assumptions are violated. We propose two novel nonparametric procedures utilizing information theory to detect significant mediation effects with high-dimensional exposures and mediators and varying data types while avoiding standard regression assumptions. Compared with available methods through comprehensive simulation studies, the proposed method shows higher power and lower error. The innovative method is applied to clinical pouchitis data as well and interesting results are obtained.

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Feature selection based on quality of information

Cited by 63 publications

References 35 publications

A tribe competition-based genetic algorithm for feature selection in pattern classification

A tribe competition-based genetic algorithm for feature selection in pattern classification

Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression

An Information-Based Approach for Mediation Analysis on High-Dimensional Metagenomic Data

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