Ensemble Data Classification based on Diversity of Classifiers Optimized by Genetic Algorithm

Thammasiri, Dech; Meesad, Phayung

doi:10.4028/www.scientific.net/amr.433-440.6572

Cited by 5 publications

(6 citation statements)

References 14 publications

(17 reference statements)

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“…The method was applied on named entity recognition datasets and achieved superior classification performance when compared to the best base classifier and two other ensemble methods. Thammasiri and Meesad [ 28 ] proposed a GA-based classifier ensemble method. From 3 types of base classifiers, they trained 30 instances by random sampling of training data, similar to how base classifiers are used in homogeneous ensembles.…”

Section: Introductionmentioning

confidence: 99%

“…The experimental outcome on three small (maximum feature and sample count was 30 and 1000 respectively) datasets from the UCI repository showed that the ensemble combination selected by the GA yielded higher performance than the individual base classifiers and two other ensemble approaches. Note that the GAs cited above have only been used in searching for optimal ensemble combination in homogeneous ensembles ([ 28 ] is also homogeneous ensemble depending on the base classifier generation approach). Hence the competency of GAs in searching for heterogeneous ensemble combinations is yet to be explored.…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

et al. 2016

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Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble’s output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) − k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer’s disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

et al. 2016

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“…Nascimento et al [21] used a combination of a genetic algorithm and AdaBoost as a heterogeneous model to lower the average error rate. Thammasiri et al [27] first used a genetic algorithm to select the best set of classifiers and then combined them using a majority vote. Antonino et al [7] combined classifiers using a genetic algorithm and then used meta-learning to optimize the combination process.…”

Section: Ensemble Systemsmentioning

confidence: 99%

“…Meta-learning is an efficient technique for predicting the best classification method for a given problem based on its meta-features [12]. Examples of meta-features are "the number of features" [7,[20][21][22]28], "the number of missing values" [27,[29][30][31][32] and "class entropy" [7,29,33]. Meta-learning uses metafeatures to describe a given problem [34].…”

Section: Meta-learningmentioning

confidence: 99%

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MEGA: Predicting the best classifier combination using meta-learning and a genetic algorithm

Golshanrad

Rahmani

Karimian

et al. 2021

IDA

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Classifier combination through ensemble systems is one of the most effective approaches to improve the accuracy of classification systems. Ensemble systems are generally used to combine classifiers; However, selecting the best combination of individual classifiers is a challenging task. In this paper, we propose an efficient assembling method that employs both meta-learning and a genetic algorithm for the selection of the best classifiers. Our method is called MEGA, standing for using MEta-learning and a Genetic Algorithm for algorithm recommendation. MEGA has three main components: Training, Model Interpretation and Testing. The Training component extracts meta-features of each training dataset and uses a genetic algorithm to discover the best classifier combination. The Model Interpretation component interprets the relationships between meta-features and classifiers using a priori and multi-label decision tree algorithms. Finally, the Testing component uses a weighted k-nearest-neighbors algorithm to predict the best combination of classifiers for unseen datasets. We present extensive experimental results that demonstrate the performance of MEGA. MEGA achieves superior results in a comparison of three other methods and, most importantly, is able to find novel interpretable rules that can be used to select the best combination of classifiers for an unseen dataset.

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From Ensemble Learning to Meta-Analytics: A Review on Trends in Business Applications

Haque

Moscato

2019

Business and Consumer Analytics: New Ideas

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Ensemble Data Classification based on Diversity of Classifiers Optimized by Genetic Algorithm

Cited by 5 publications

References 14 publications

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

MEGA: Predicting the best classifier combination using meta-learning and a genetic algorithm

From Ensemble Learning to Meta-Analytics: A Review on Trends in Business Applications

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