NDLI: FACISME: Fuzzy associative classification using iterative scaling and maximum entropy

Content Provider	IEEE Xplore Digital Library
Author	Mangalampalli, A. Pudi, V.
Copyright Year	2010
Description	Author affiliation: Centre for Data Engineering, International Institute of Information Technology(IIIT), Hyderabad-500032, India (Mangalampalli, A.) \|\| Centre for Data Engineering, International Institute of Information Technology (IIIT), Hyderabad-500032, India (Pudi, V.)
Abstract	All associative classifiers developed till now are crisp in nature, and thus use sharp partitioning to transform numerical attributes to binary ones like “Income = [100K and above]”. On the other hand, the novel fuzzy associative classification algorithm called FACISME, which we propose in this paper, uses fuzzy logic to convert numerical attributes to fuzzy attributes, like “Income = High”, thus maintaining the integrity of information conveyed by such numerical attributes. Moreover, FACISME is based on maximum entropy, and uses iterative scaling, both of which lend a very strong theoretical foundation to the algorithm. Entropy is one of the best measures of information, and maximum-entropy-based algorithms do not assume independence of parameters in the classification process. Thus, FACISME provides very good accuracy, and can work with all types of datasets (irrespective of size and type of attributes — numerical or binary) and domains.
Starting Page	1
Ending Page	8
File Size	768081
Page Count	8
File Format	PDF
ISBN	9781424469192
ISSN	10987584
e-ISBN	9781424469215
DOI	10.1109/FUZZY.2010.5584127
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-18
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Classification algorithms Entropy Itemsets Geographic Information Systems Association rules Mathematical model Training
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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