Dynamic classifier chains for multi-label learning

2017-10-20 · Pawel Trajdos, Marek Kurzynski

In this paper, we deal with the task of building a dynamic ensemble of chain classifiers for multi-label classification. To do so, we proposed two concepts of classifier chains algorithms that are able to change label order of the chain without rebuilding the entire model. Such modes allows anticipating the instance-specific chain order without a significant increase in computational burden. The proposed chain models are built using the Naive Bayes classifier and nearest neighbour approach as a base single-label classifiers. To take the benefits of the proposed algorithms, we developed a simple heuristic that allows the system to find relatively good label order. The heuristic sort labels according to the label-specific classification quality gained during the validation phase. The heuristic tries to minimise the phenomenon of error propagation in the chain. The experimental results showed that the proposed model based on Naive Bayes classifier the above-mentioned heuristic is an efficient tool for building dynamic chain classifiers.

📄 PDF Abstract BibTeX arXiv:1710.07491

Code (0)

등록된 구현이 없습니다.

Tasks

General Classification Multi-Label Classification MUlTI-LABEL-ClASSIFICATION Multi-Label Learning

Tree-Based Dynamic Classifier Chains

2021-12-13 · Eneldo Loza Mencía, Moritz Kulessa, Simon Bohlender, Johannes Fürnkranz

Classifier chains are an effective technique for modeling label dependencies in multi-label classification. However, the method requires a fixed, static order of the labels. While in theory, any order is sufficient, in p…

Multi-Label Classification MUlTI-LABEL-ClASSIFICATION

Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains

2020-06-15 · Bohlender, Simon, Loza Mencia, Eneldo 외

Classifier chains is a key technique in multi-label classification, since it allows to consider label dependencies effectively. However, the classifiers are aligned according to a static order of the labels. In the conce…

Multi-Label Classification MUlTI-LABEL-ClASSIFICATION

Exploring label correlations using decision templates for ensemble of classifier chains

2026-03-13 · Victor F. Rocha, Alexandre L. Rodrigues, Thiago Oliveira-Santos, Flávio M. Varejão arxiv

The use of ensemble-based multi-label methods has been shown to be effective in improving multi-label classification results. One of the most widely used ensemble-based multi-label classifiers is Ensemble of Classifier C…

Multi-Label Classification

Label Cluster Chains for Multi-Label Classification

2024-11-01 · Elaine Cecília Gatto, Felipe Nakano Kenji, Jesse Read, Mauri Ferrandin 외

Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. To solve this task, some methods divide the original problem into several sub-problems (l…

Classification Multi-Label Classification MUlTI-LABEL-ClASSIFICATION

A Three-phase Augmented Classifiers Chain Approach Based on Co-occurrence Analysis for Multi-Label Classification

2022-04-13 · Gao Pengfei, Lai Dedi, Zhao Lijiao, Liang Yue 외

As a very popular multi-label classification method, Classifiers Chain has recently been widely applied to many multi-label classification tasks. However, existing Classifier Chains methods are difficult to model and exp…