Zoomed anking: Selection of Classification Algorithms Based on Relevant Performance Information

• Type: Article in International Scientific Journal
• Date: 2000

Authors

Carlos Soares (Author) FEP; Pavel B Brazdil (Author) FEP

Journal

Title: Lecture Notes in Computer Science

Vol. 1910

Pages: 126-135

ISSN: 0302-9743

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 12 Citations

Scopus - 56 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-001-1VB

DOI: 10.1007/3-540-45372-5_13

Abstract (EN): Given the wide variety of available classification algorithms and the volume of data today's organizations need to analyze, the selection of the right algorithm to use on a new problem is an important issue. In this paper we present a combination of techniques to address this problem. The first one, zooming, analyzes a given dataset and selects relevant (similar) datasets that were processed by the candidate algoritms in the past. This process is based on the concept of distance, calculated on the basis of several dataset characteristics. The information about the performance of the candidate algorithms on the selected datasets is then processed by a second technique, a ranking method. Such a method uses performance information to generate advice in the form of a ranking, indicating which algorithms should be applied in which order. Here we propose the adjusted ratio of ratios ranking method. This method takes into account not only accuracy but also the time performance of the candidate algorithms. The generalization power of this ranking method is analyzed. For this purpose, an appropriate methodology is defined. The experimental results indicate that on average better results are obtained with zooming than without it.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10