Journal of Machine Learning Research· 2014Q1
Do we need hundreds of classifiers to solve real world classification problems
- 2,181citations
- Q1SCImago
- 2014year
Short summary
A comprehensive evaluation of 179 classifiers across 17 families reveals that a small subset of ensemble methods (boosting, bagging, stacking) and kernel-based methods (SVMs) consistently outperform others on real-world datasets.
AI-generated from the title and abstract; the full text is not read.
Key points
- 179 classifiers from 17 families were evaluated on real-world classification datasets.
- Ensemble methods (boosting, bagging, stacking) and SVMs demonstrated consistently high performance.
- These top-performing methods often outperformed simpler algorithms.
- The study provides empirical evidence for selecting effective classifiers.
AI-generated from the title and abstract; the full text is not read.
Abstract
We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking,...
The authors' abstract, as published at the source. Journal of Machine Learning Research, 2014 · DOI ↗
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Field: Information Systems
Information SystemsComputer Science