Evolving Classifier Ensemble With Gene Expression Programming

Li, Qu; Wang, Weihong; Han, Shanshan; Li, Jianhong

doi:10.1109/icnc.2007.362

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…Use of data mining models been used in petroleum engineering for viscosity estimation [35,36]. Te multivariate adaptive regression splines (MARS) analysis model, unlike the (GEP) model, is little used in health issues; it is a nonparametric modelling method developed systematically and automatically without limitation of the assumptions that traditional regression models must meet [37].…”

Section: Grouping Of Variablesmentioning

confidence: 99%

Sound Pressure Level at Terminals through Data Mining

Monteza¹,

Rivera²,

Huatangari³

et al. 2023

Scientific Programming

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The land transportation is a cause of noise in cities, thus breaking the natural balance and bringing with it physiological and mental illnesses, as well as occupational accidents. In this sense, the objective of the research was to estimate the sound pressure in land terminals in the city of Jaen, Peru, using data mining algorithms. The methodology consisted in environmentally monitoring six terminals in the city of Jaen, during 2019, using a class 1 sound level meter; the exploratory analysis of the collected variables that influence the noise of the terminals (minimum and maximum sound pressure level, number of light and heavy vehicles, and equivalent sound pressure level) was performed, which were grouped into three groups of variables for the purpose of using data mining algorithms. Three algorithms were used, namely, artificial neural network, linear regression, and M5Rules, using the free software Weka. Considering all variables, the M5Rules method performed the best, because the value of the mean absolute error (0.7462), the root mean square error (1.0575), and uncertainty analysis (0.09) was the smallest compared to the other two methods. However, for the two remaining groups of variables, the linear regression model showed the lowest mean absolute error and mean square root of the error; in addition to presenting coefficients of determination close to one. The algorithms show good behavior when estimating the sound pressure of the terminals in the city of Jaen.

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Section: Grouping Of Variablesmentioning

confidence: 99%

Sound Pressure Level at Terminals through Data Mining

Monteza¹,

Rivera²,

Huatangari³

et al. 2023

Scientific Programming

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“…In Ref. 16, two well-known ensemble techniques À À À bagging and boostingwere used to enhance the generalization ability of GEP classi¯ers. Di®erent approach to building GEP-based classi¯er ensembles was proposed by Wu et al 17 The idea was to construct weak classi¯ers from di®erent subsets of attributes controlling the diversity among these subsets through applying a variant of niching technique.…”

Section: Related Workmentioning

confidence: 99%

Implementing Gene Expression Programming in the Parallel Environment for Big Datasets’ Classification

Jȩdrzejowicz

Jędrzejowicz

Wierzbowska

2019

Vietnam J. Comp. Sci.

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The paper investigates a Gene Expression Programming (GEP)-based ensemble classifier constructed using the stacked generalization concept. The classifier has been implemented with a view to enable parallel processing with the use of Spark and SWIM — an open source genetic programming library. The classifier has been validated in computational experiments carried out on benchmark datasets. Also, it has been inbvestigated how the results are influenced by some settings. The paper is an extension of a previous paper of the authors.

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“…An improvement of the basic GEP classifiers can be achieved by combining GEP-induced weak classifiers into a classifier ensemble. In [37] two well-known ensemble techniques, bagging and boosting, were used to enhance the generalization ability of GEP classifiers. Yet another approach to building GEP-based classifier ensembles was proposed in [38].…”

Section: Related Workmentioning

confidence: 99%

Incremental Gene Expression Programming Classifier with Metagenes and Data Reduction

Jȩdrzejowicz

Jędrzejowicz

2018

Complexity

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The paper proposes an incremental Gene Expression Programming classifier. Its main features include using two-level ensemble consisting of base classifiers in form of genes and the upper-level classifier in the form of metagene. The approach enables us to deal with big datasets through controlling computation time using data reduction mechanisms. The user can control the number of attributes used to induce base classifiers as well as the number of base classifiers used to induce metagenes. To optimize the parameter setting phase, an approach based on the Orthogonal Experiment Design principles is proposed, allowing for statistical evaluation of the influence of different factors on the classifier performance. In addition, the algorithm is equipped with a simple mechanism for drift detection. A detailed description of the algorithm is followed by the extensive computational experiment. Its results validate the approach. Computational experiment results show that the proposed approach compares favourably with several state-of-the-art incremental classifiers.

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Evolving Classifier Ensemble With Gene Expression Programming

Cited by 8 publications

References 5 publications

Sound Pressure Level at Terminals through Data Mining

Sound Pressure Level at Terminals through Data Mining

Implementing Gene Expression Programming in the Parallel Environment for Big Datasets’ Classification

Incremental Gene Expression Programming Classifier with Metagenes and Data Reduction

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