Fusing Decision Trees Based on Genetic Programming for Classification of Microarray Datasets

Liu, Kun-Hong; Tong, Muchenxuan; Xie, Su‐Yuan; Zeng, Zhihao

doi:10.1007/978-3-319-09339-0_13

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…One of the most popular EA-based nonlinear methods are expression trees (Escalante et al, 2013;Tsakonas, 2014;Liu et al, 2014aLiu et al, , 2015Lacy et al, 2015b,a;Folino et al, 2016). Expression trees have models in their leaves and combination operators in their inner nodes.…”

Section: Expression Treesmentioning

confidence: 99%

“…For the problem of microarray data classification, in Liu et al (2015Liu et al ( , 2014a) some decision trees (initially trained with bagging) are fed to a Genetic Programming algorithm, which then induces a population of expression trees (each allowed to have at most 3 levels) for combining the base classifiers' votes. After the evolutionary process is completed, expression trees with accuracy higher than the average are selected by a forward-search algorithm to compose the final meta-committee, which will predict the class of unknown instances.…”

Section: Expression Treesmentioning

confidence: 99%

“…Several application domains have benefited from EA-based ensemble learning algorithms, including for example wind speed prediction Zhang et al, 2017a), cancer detection (Krawczyk et al, 2013;Krawczyk et al, 2015Krawczyk et al, , 2016Singh et al, 2016), noise bypass detection in vehicles (Redel-Macías et al, 2013), stock market prediction (Chen et al, 2007;Mabu et al, 2014Mabu et al, , 2015, and microarray data classification (Park & Cho, 2003bLiu et al, 2007Liu et al, , 2014aLiu et al, , 2015Rapakoulia et al, 2014;Saha et al, 2016), to name just a few.…”

Section: Introductionmentioning

confidence: 99%

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A survey of evolutionary algorithms for supervised ensemble learning

Cagnini

Dôres

Freitas

et al. 2023

The Knowledge Engineering Review

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This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning (classification and regression). We propose a detailed four-level taxonomy of studies in this area. The first level of the taxonomy categorizes studies based on which stage of the ensemble learning process is addressed by the evolutionary algorithm: the generation of base models, model selection, or the integration of outputs. The next three levels of the taxonomy further categorize studies based on methods used to address each stage. In addition, we categorize studies according to the main types of objectives optimized by the evolutionary algorithm, the type of base learner used and the type of evolutionary algorithm used. We also discuss controversial topics, like the pros and cons of the selection stage of ensemble learning, and the need for using a diversity measure for the ensemble’s members in the fitness function. Finally, as conclusions, we summarize our findings about patterns in the frequency of use of different methods and suggest several new research directions for evolutionary ensemble learning.

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Section: Expression Treesmentioning

confidence: 99%

Section: Expression Treesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A survey of evolutionary algorithms for supervised ensemble learning

Cagnini

Dôres

Freitas

et al. 2023

The Knowledge Engineering Review

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show abstract

“…Some of the widely used classifiers in machine learning techniques are SVM classifiers [7], Decision Trees [8], Naive Bayes [9] and Linear Regression [10] and Random Forest Trees [11]. When larger datasets are being used in an application then the use of Artificial Neural Networks (ANNs) is preferred for feature extraction as it produces more accurate results [12].…”

Section: Introductionmentioning

confidence: 99%

Rainfall Prediction using Regression Model

2019

ijrte

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Artificial intelligence is been widely used in all the applications and weather forecasting is not an exception. When it comes to weather forecasting, rainfall prediction is one of the most widely used research areas as numerous lives and property damages occur due to this. Intense rainfall has abundant impacts on society and on our daily life from cultivation to disaster measures. Previous rainfall prediction models that are widely used, makes use of many the complicated blend of mathematical instruments which was insufficient to get a higher classification rate. In this project, we propose new novel methods for predicting monthly rainfall using linear regressionanalysis. Rainfall predictions are made by collecting quantitative data about the current state of the atmosphere. Numerous machine learning algorithms can learn complex mappings from inputs to outputs, based solely on samples and require limited. Accurate prediction of rainfall is a difficult task due to the dynamic nature of the atmosphere. To predict the future’s rainfall condition, the variation in the conditions in past years must be utilized. We have proposed the use of linear regressions by making use of various parameters such as temperature, humidity,and wind. The proposed model tends to forecast rainfall based on the previous records of a particular geographic area, therefore, this prediction will prove to be much reliable. The performance of the model is more accurate when compared with traditional rainfall prediction systems

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“…It should be noted that although our ensemble system only employs decision trees, other classifiers, such as SVM and neural network, can also be used in this framework. Some earlier works in this paper have been presented at ICIC 2014 conference [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Genetic Programming Based Ensemble System for Microarray Data Classification

Liu

Tong

Xie

et al. 2015

Computational and Mathematical Methods in Medicine

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Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.

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Fusing Decision Trees Based on Genetic Programming for Classification of Microarray Datasets

Cited by 5 publications

References 15 publications

A survey of evolutionary algorithms for supervised ensemble learning

A survey of evolutionary algorithms for supervised ensemble learning

Rainfall Prediction using Regression Model

Genetic Programming Based Ensemble System for Microarray Data Classification

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