Experimental Comparison of Methods for Multi-label Classification in different Application Domains
نویسندگان
چکیده
منابع مشابه
A Comparative Analysis of Classification Methods to Multi-label Tasks in Different Application Domains
In traditional classification problems (single-label), patterns are usually associated with a single label from a set of two or more classes. When an example can simultaneously belong to more than one class (label), this classification problem is known as multi-label classification problem. Multi-label classification methods have been increasingly used in modern applications, such as music cate...
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An extensive experimental comparison of methods for multi-label learning
Multi-label learning has received significant attention in the research community over the past few years: this has resulted in the development of a variety of multi-label learning methods. In this paper, we present an extensive experimental comparison of 12 multi-label learning methods using 16 evaluation measures over 11 benchmark datasets. We selected the competing methods based on their pre...
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Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks. In the RAndom k-labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of elements in the subset. In this paper we adopt a similar approach, however, inst...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2015
ISSN: 0975-8887
DOI: 10.5120/20083-1666