DeepAI AI Chat
Log In Sign Up

Ensemble Learning Based Classification Algorithm Recommendation

by   Guangtao Wang, et al.

Recommending appropriate algorithms to a classification problem is one of the most challenging issues in the field of data mining. The existing algorithm recommendation models are generally constructed on only one kind of meta-features by single learners. Considering that i) ensemble learners usually show better performance and ii) different kinds of meta-features characterize the classification problems in different viewpoints independently, and further the models constructed with different sets of meta-features will be complementary with each other and applicable for ensemble. This paper proposes an ensemble learning-based algorithm recommendation method. To evaluate the proposed recommendation method, extensive experiments with 13 well-known candidate classification algorithms and five different kinds of meta-features are conducted on 1090 benchmark classification problems. The results show the effectiveness of the proposed ensemble learning based recommendation method.


MxML: Mixture of Meta-Learners for Few-Shot Classification

A meta-model is trained on a distribution of similar tasks such that it ...

An ensemble learning framework based on group decision making

The classification problem is a significant topic in machine learning wh...

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

Imbalanced learning (IL), i.e., learning unbiased models from class-imba...

Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining

The notion of meta-mining has appeared recently and extends the traditio...

Diagnosing Ensemble Few-Shot Classifiers

The base learners and labeled samples (shots) in an ensemble few-shot cl...

A Meta-learning based Distribution System Load Forecasting Model Selection Framework

This paper presents a meta-learning based, automatic distribution system...

Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms

The need to evaluate treatment effectiveness is ubiquitous in most of em...