DocumentCode :
2313913
Title :
Fast Preliminary Evaluation of New Machine Learning Algorithms for Feasibility
Author :
Baumgartner, Dustin ; Serpen, Gursel
Author_Institution :
Electr. Eng. & Comput. Sci. Dept., Univ. of Toledo, Toledo, OH, USA
fYear :
2010
fDate :
9-11 Feb. 2010
Firstpage :
113
Lastpage :
115
Abstract :
Traditionally, researchers compare the performance of new machine learning algorithms against those of locally executed simulations that serve as benchmarks. This process requires considerable time, computation resources, and expertise. In this paper, we present a method to quickly evaluate the performance feasibility of new algorithms - offering a preliminary study that either supports or opposes the need to conduct a full-scale traditional evaluation, and possibly saving valuable resources for researchers. The proposed method uses performance benchmarks obtained from results reported in the literature rather than local simulations. Furthermore, an alternate statistical technique is suggested for comparative performance analysis, since traditional statistical significance tests do not fit the problem well. We highlight the use of the proposed evaluation method in a study that compared a new algorithm against 47 other algorithms across 46 datasets.
Keywords :
learning (artificial intelligence); statistical testing; fast preliminary evaluation; machine learning; performance analysis; performance feasibility; statistical significance tests; statistical technique; Algorithm design and analysis; Benchmark testing; Computational modeling; Computer science; Machine learning; Machine learning algorithms; Performance analysis; Robustness; Statistical analysis; learning algorithm; performance evaluation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Computing (ICMLC), 2010 Second International Conference on
Conference_Location :
Bangalore
Print_ISBN :
978-1-4244-6006-9
Electronic_ISBN :
978-1-4244-6007-6
Type :
conf
DOI :
10.1109/ICMLC.2010.31
Filename :
5460759
Link To Document :
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