Impact of Feature Selection on Average Ranking Method via Metalearning

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Abdulrahman, SM (Author) Other; Cachada, MV (Author) Other; Pavel Brazdil (Author) FEP

Conference proceedings International

Title: European Congress on Computational Methods in Applied Sciences and Engineering: ECCOMAS 2017: VipIMAGE 2017

Pages: 1091-1101

6th ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE)

Porto, PORTUGAL, OCT 18-20, 2017

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Other information

Authenticus ID: P-00N-5WX

DOI: 10.1007/978-3-319-68195-5_121

Abstract (EN): Selecting appropriate classification algorithms for a given dataset is crucial and useful in practice but is also full of challenges. In order to maximize performance, users of machine learning algorithms need methods that can help them identify the most relevant features in datasets, select algorithms and determine their appropriate hyperparameter settings. In this paper, a method of recommending classification algorithms is proposed. It is oriented towards the average ranking method, combining algorithm rankings observed on prior datasets to identify the best algorithms for a new dataset. Our method uses a special case of data mining workflow that combines algorithm selection preceded by a feature selection method (CFS).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11