Feature Selection Based on Density Peak Clustering Using Information Distance Measure
| Autor: | Shu-Lin Wang, Sheng Yang, Shilong Chao, Jiawei Luo, Jie Cai |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
business.industry Correlation clustering Feature selection Pattern recognition 02 engineering and technology 01 natural sciences Measure (mathematics) Information distance 010104 statistics & probability ComputingMethodologies_PATTERNRECOGNITION Feature (computer vision) Metric (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Data pre-processing 0101 mathematics business Cluster analysis k-medians clustering |
| Zdroj: | Intelligent Computing Theories and Application ISBN: 9783319633114 ICIC (2) |
| Popis: | Feature selection is one of the most important data preprocessing techniques in data mining and machine learning. A new feature selection method based on density peak clustering is proposed. The new method applies an information distance between features as clustering distance metric, and uses the density peak clustering method for feature clustering. The representative feature of each cluster is selected to generate the final result. The method can avoid selecting the irrelevant representative feature from one cluster, where most features are irrelevant to class label. The comparison experiments on ten datasets show that the feature selection results of the proposed method exhibit improved classification accuracies for different classifiers. |
| Databáze: | OpenAIRE |
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