Feature selection algorithm ensembling based on meta-learning

Tanfilev, Igor; Filchenkov, Andrey; Smetannikov, Ivan

doi:10.1109/cisp-bmei.2017.8302301

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…An ensemble system is a multi-classifier system that aims at combining different classifiers to achieve a higher level of efficiency than individual classifiers achieve [6,7,[15][16][17].…”

Section: Ensemble Systemsmentioning

confidence: 99%

“…While the resulting performances of both G1 and G2 are acceptable, -There is no classifier combination applied in G1 and they explore only hypothesis spaces limited to the selected classifier. However, it is shown that in most cases classifiers combination generates better accuracy results comparing to individual use of classifiers [6,7,[15][16][17]. As a result, we concentrate on classifiers combination in this paper.…”

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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“…An ensemble system is a multi-classifier system that aims at combining different classifiers to achieve a higher level of efficiency than individual classifiers achieve [6,7,[15][16][17].…”

Section: Ensemble Systemsmentioning

confidence: 99%

Section: Meta-learningmentioning

confidence: 99%

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

Golshanrad

Rahmani

Karimian

et al. 2021

IDA

View full text Add to dashboard Cite

show abstract

“…Along with the well-known meta-learning approaches there are novel approaches based on techniques like morphing [36] which transforms data and observes changes in behavior of learning algorithms. Meta-learning is also used for other data interoperability tasks such as feature selection [37], [38]. The meta-features presented in this paper are This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: Meta-featuresmentioning

confidence: 99%

Novel Meta-Features for Automated Machine Learning Model Selection in Anomaly Detection

et al. 2021

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A growing number of research papers shed light on automated machine learning (AutoML) frameworks, which are becoming a promising solution for building complex machine learning models without human expertise and assistance. The key challenge in enabling AutoML frameworks to build an efficient model for anomaly detection tasks is to determine the best underlying model for a given task and optimization metric. The meta-learning approaches based on a set of meta-features that describes data properties can enable efficient model selection in AutoML frameworks. The existing meta-learning approaches based on statistical and information-theoretic meta-features require large amounts of data and computational resources to extract data properties. This paper proposes a novel set of meta-features for model selection in anomaly detection tasks based on domain-specific properties of data which overcomes the shortcomings of existing meta-features by introducing simple but effective meta-features that can be efficiently extracted or estimated by using a low amount of data. Experiments with 63 datasets from different repositories with varying schemas show that the proposed set of meta-features achieves an accuracy of 87% for model selection, while the achieved accuracy for simple meta-features is 74%, for statistical meta-features 68%, for information theory meta-feature 70%, and for a comprehensive set of meta-features by pyMFE 73%. This demonstrates that the proposed set can be adopted by AutoML frameworks across a diverse range of domains.

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