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Ml-knn: A lazy learning approach to multi-label learning (2007)
| Venue: | PATTERN RECOGNITION |
| Citations: | 184 - 21 self |
BibTeX
@ARTICLE{Zhang07ml-knn:a,
author = {Min-ling Zhang and Zhi-hua Zhou},
title = { Ml-knn: A lazy learning approach to multi-label learning},
journal = {PATTERN RECOGNITION},
year = {2007},
volume = {40},
pages = {2007}
}
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Abstract
Multi-label learning originated from the investigation of text categorization problem, where each document may belong to several predefined topics simultaneously. In multi-label learning, the training set is composed of instances each associated with a set of labels, and the task is to predict the label sets of unseen instances through analyzing training instances with known label sets. In this paper, a multi-label lazy learning approach named Mlknn is presented, which is derived from the traditional k-Nearest Neighbor (kNN) algorithm. In detail, for each unseen instance, its k nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle is utilized to determine the label set for the unseen instance. Experiments on three different real-world multi-label learning problems, i.e. Yeast gene functional analysis, natural scene classification and automatic web page categorization, show that Ml-knn achieves superior performance to some well-established multi-label learning algorithms.
Keyphrases
multi-label learning unseen instance neighboring instance label set statistical information training set traditional k-nearest neighbor yeast gene functional analysis superior performance possible class automatic web page categorization natural scene classification known label set different real-world multi-label learning problem several predefined topic training instance multi-label lazy text categorization problem well-established multi-label learning algorithm
