Book ChapterDOI
Selective Ensemble of Classifier Chains
Nan Li,Nan Li,Zhi-Hua Zhou +2 more
- pp 146-156
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TLDR
This paper proposes selective ensemble of classifier chains (SECC) which tries to select a subset of classifiers to composite the ensemble whilst keeping or improving the performance, and forms this problem as a convex optimization problem which can be efficiently solved by the stochastic gradient descend method.Abstract:
In multi-label learning, the relationship among labels is well accepted to be important, and various methods have been proposed to exploit label relationships. Amongst them, ensemble of classifier chains (ECC) which builds multiple chaining classifiers by random label orders has drawn much attention. However, the ensembles generated by ECC are often unnecessarily large, leading to extra high computational and storage cost. To tackle this issue, in this paper, we propose selective ensemble of classifier chains (SECC) which tries to select a subset of classifier chains to composite the ensemble whilst keeping or improving the performance. More precisely, we focus on the performance measure F1-score, and formulate this problem as a convex optimization problem which can be efficiently solved by the stochastic gradient descend method. Experiments show that, compared with ECC, SECC is able to obtain much smaller ensembles while achieving better or at least comparable performance.read more
Citations
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Book
Multiple Classifier Systems
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Journal ArticleDOI
A survey on ensemble learning
TL;DR: Challenges and possible research directions for each mainstream approach of ensemble learning are presented and an extra introduction is given for the combination of ensemblelearning with other machine learning hot spots such as deep learning, reinforcement learning, etc.
Journal ArticleDOI
Binary relevance for multi-label learning: an overview
TL;DR: This paper aims to review the state of the art of binary relevance from three perspectives, and some of the recent studies on binary relevance aimed at issues other than label correlation exploitation are introduced.
Proceedings Article
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification
TL;DR: This paper replaces classifier chains with recurrent neural networks, a sequence-to-sequence prediction algorithm which has recently been successfully applied to sequential prediction tasks in many domains, and compares different ways of ordering the label set, and gives some recommendations on suitable ordering strategies.
Journal ArticleDOI
Learning rules for multi-label classification: a stacking and a separate-and-conquer approach
TL;DR: Two approaches for learning label-dependent rules are introduced and it is shown that the discovered dependencies contribute to the understanding and improve the analysis of multi-label datasets, and that the found multi- label rules are crucial for the predictive performance as the proposed approaches beat the baseline using conventional rules.
References
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Learning multi-label scene classification
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BoosTexter: A Boosting-based Systemfor Text Categorization
Robert E. Schapire,Yoram Singer +1 more
TL;DR: In this article, a new and improved family of boosting algorithms is proposed for text categorization tasks, called BoosTexter, which learns from examples to perform multiclass text and speech categorization.