# Ensemble Learning Based Classification Algorithm Recommendation

arXiv: https://arxiv.org/abs/2101.05993 · HTML: https://mlsanity.com/paper/2101.05993/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 77 references that are in the map.
- First posted on arXiv 15 Jan 2021.

## Builds on (most cited first, 1 of 1)

- [Ensemble Methods for Multi-label Classification](https://mlsanity.com/paper/1307.1769/) (2013)

## Built on by (most cited first, 1 of 1)

- [SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking](https://mlsanity.com/paper/2503.00955/) (2025)

## Abstract (arXiv)

Selecting an appropriate classification algorithm for a given data set remains a challenging problem in data mining and machine learning. Existing algorithm recommendation models are typically trained with individual learners and rely on only one type of meta-feature, which may limit their ability to capture the diverse characteristics of classification problems. This paper proposes a multi-view ensemble meta-learning framework for classification algorithm recommendation. The framework constructs base recommendation models from different combinations of heterogeneous meta-feature groups and combines them through an accuracy- and diversity-aware ensemble strategy. The main focus of this work is empirical: we evaluate the proposed method on 1,090 benchmark classification problems derived from 84 public data sets, using 13 widely used candidate classification algorithms and five types of meta-features. The experimental results show that the proposed ensemble recommendation method consistently improves ranking loss, average precision, and top-ranked recommendation precision over individual recommendation models. These results suggest that combining complementary meta-feature views is an effective strategy for robust classification algorithm recommendation.