Duo-LDL method for Label Distribution Learning based on pairwise class dependencies
| Autor: | Jacek Mańdziuk, Adam Żychowski |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Theoretical computer science Artificial neural network Generalization Computer science 02 engineering and technology Object (computer science) Class (biology) Multiclass classification ComputingMethodologies_PATTERNRECOGNITION 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Feature (machine learning) 020201 artificial intelligence & image processing Pairwise comparison Computer Science::Databases Software |
| Zdroj: | Applied Soft Computing. 110:107585 |
| ISSN: | 1568-4946 |
| Popis: | Label Distribution Learning (LDL) is a new learning paradigm with numerous applications in various domains. It is a generalization of both standard multiclass classification and multilabel classification. Instead of a binary value, in LDL, each label is assigned a real number which corresponds to a degree of membership of the object being classified to a given class. In this paper a new neural network approach to Label Distribution Learning (Duo-LDL), which considers pairwise class dependencies, is introduced. The method is extensively tested on 15 well-established benchmark sets, against 6 evaluation measures, proving its competitiveness to state-of-the-art non-neural LDL approaches. Additional experimental results on artificially generated data demonstrate that Duo-LDL is especially effective in the case of most challenging benchmarks, with extensive input feature representations and numerous output classes. |
| Databáze: | OpenAIRE |
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