Effective Multi-label Classification Method for Multidimensional Datasets
| Autor: | Kinga Glinka, Danuta Zakrzewska |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Multi-label classification
0209 industrial biotechnology Computer science business.industry Problem transformation Computation Small number Process (computing) Binary number Pattern recognition 02 engineering and technology Class (biology) ComputingMethodologies_PATTERNRECOGNITION 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Relevance (information retrieval) Artificial intelligence business |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783319261539 FQAS |
| DOI: | 10.1007/978-3-319-26154-6_10 |
| Popis: | Multi-label classification, contrarily to the traditional single-label one, aims at predicting more than one predefined class label for data instances. Multi-label classification problems very often concern multidimensional datasets where number of attributes significantly exceeds relatively small number of instances. In the paper, new effective problem transformation method which deals with such cases is introduced. The proposed Labels Chain (LC) algorithm is based on relationship between labels, and consecutively uses result labels as new attributes in the following classification process. Experiments conducted on several multidimensional datasets showed the good performance of the presented method, taking into account predictive accuracy and computation time. The obtained results are compared with those obtained by the most popular Binary Relevance (BR) and Label Power-set (LP) algorithms. |
| Databáze: | OpenAIRE |
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