Distance-based features in pattern classification
| Autor: | Wei-Yang Lin, Zhen-Fu Hong, Chih-Fong Tsai, Chung-Yang Hsieh |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Computer science
Feature extraction feature representation lcsh:TK7800-8360 computer.software_genre distance-based features lcsh:Telecommunication Naive Bayes classifier pattern classification cluster center lcsh:TK5101-6720 Representation (mathematics) business.industry feature extraction lcsh:Electronics Pattern recognition data mining Support vector machine ComputingMethodologies_PATTERNRECOGNITION Feature (computer vision) Artificial intelligence Data mining business Focus (optics) computer Distance based Curse of dimensionality |
| Zdroj: | EURASIP Journal on Advances in Signal Processing, Vol 2011, Iss 1, p 62 (2011) |
| ISSN: | 1687-6180 1687-6172 |
| Popis: | In data mining and pattern classification, feature extraction and representation methods are a very important step since the extracted features have a direct and significant impact on the classification accuracy. In literature, numbers of novel feature extraction and representation methods have been proposed. However, many of them only focus on specific domain problems. In this article, we introduce a novel distance-based feature extraction method for various pattern classification problems. Specifically, two distances are extracted, which are based on (1) the distance between the data and its intra-cluster center and (2) the distance between the data and its extra-cluster centers. Experiments based on ten datasets containing different numbers of classes, samples, and dimensions are examined. The experimental results using naïve Bayes, k-NN, and SVM classifiers show that concatenating the original features provided by the datasets to the distance-based features can improve classification accuracy except image-related datasets. In particular, the distance-based features are suitable for the datasets which have smaller numbers of classes, numbers of samples, and the lower dimensionality of features. Moreover, two datasets, which have similar characteristics, are further used to validate this finding. The result is consistent with the first experiment result that adding the distance-based features can improve the classification performance. |
| Databáze: | OpenAIRE |
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