An active learning algorithm for multi-class classification
| Autor: | Yanbi Liu, Dongjiang Liu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Active learning (machine learning) Process (computing) 020207 software engineering 02 engineering and technology Task (project management) Multiclass classification Support vector machine Artificial Intelligence Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Algorithm Energy (signal processing) |
| Zdroj: | Pattern Analysis and Applications. 22:1051-1063 |
| ISSN: | 1433-755X 1433-7541 |
| DOI: | 10.1007/s10044-018-0716-1 |
| Popis: | Since the number of instances in the training set is very large, data annotating task consumes plenty of time and energy. Active learning algorithms can efficiently reduce the number of instances that need to be annotated. In this paper, authors propose a new active learning algorithm. The algorithm is mainly proposed for multi-class classification model based on support vector machine (SVM). In the algorithm, the unlabeled instances that can promote several SVM classifiers in the multi-class classification model will be selected firstly. So when one newly selected instance is added into training set, more than one classification hyper-planes in the multi-class classification model will be promoted. During the process of instance selection, the algorithm also tries to choose the instance that is least similar with the instances that have already been annotated. In this way, the instances selected by the algorithm for annotating will perfectly represent the feature of the whole dataset. |
| Databáze: | OpenAIRE |
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