I-prune: Item selection for associative classification
نویسندگان
چکیده
منابع مشابه
I-prune: Item selection for associative classification
Associative classification is characterized by accurate models and high model generation time. Most time is spent in extracting and post-processing a large set of irrelevant rules, which are eventually pruned. We propose I-prune, an item pruning approach that selects uninteresting items by means of an interestingness measure and prunes them as soon as they are detected. Thus, the number of extr...
متن کاملA tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation
Associative-classification is a promising classification method based on association-rule mining. Significant amount of work has already been dedicated to the process of building a classifier based on association rules. However, relatively small amount of research has been performed in association-rule mining from multi-label data. In such data each example can belong, and thus should be classi...
متن کاملMutual Information Item Selection in Adaptive Classification Testing
at: can be found Educational and Psychological Measurement Additional services and information for http://epm.sagepub.com/cgi/alerts Email Alerts: http://epm.sagepub.com/subscriptions Subscriptions: http://www.sagepub.com/journalsReprints.nav Reprints: http://www.sagepub.com/journalsPermissions.nav Permissions: http://epm.sagepub.com/cgi/content/refs/67/1/41 SAGE Journals Online and HighWire ...
متن کاملAssociative Classification Mining for Website Phishing Classification
-Website phishing is one of the crucial research topics for the internet community due to the massive number of online daily transactions. The process of predicting the phishing activity for a website is a typical classification problem in data mining where different website’s features such as URL length, prefix and suffix, IP address, etc., are used to discover concealed correlations (knowledg...
متن کاملSFLA Based Gene Selection Approach for Improving Cancer Classification Accuracy
In this paper, we propose a new gene selection algorithm based on Shuffled Frog Leaping Algorithm that is called SFLA-FS. The proposed algorithm is used for improving cancer classification accuracy. Most of the biological datasets such as cancer datasets have a large number of genes and few samples. However, most of these genes are not usable in some tasks for example in cancer classification....
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Intelligent Systems
سال: 2012
ISSN: 0884-8173
DOI: 10.1002/int.21524