Classifying networked entities with modularity kernels
Proceedings of the 17th ACM conference on Information and knowledge management
Calibrated lazy associative classification
SBBD '08 Proceedings of the 23rd Brazilian symposium on Databases
A Multi-Strategy Approach to KNN and LARM on Small and Incrementally Induced Prediction Knowledge
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Efficient itemset generator discovery over a stream sliding window
Proceedings of the 18th ACM conference on Information and knowledge management
Learning to rank for content-based image retrieval
Proceedings of the international conference on Multimedia information retrieval
Effective self-training author name disambiguation in scholarly digital libraries
Proceedings of the 10th annual joint conference on Digital libraries
Constructing classification features using minimal predictive patterns
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Computer Methods and Programs in Biomedicine
Time series shapelets: a novel technique that allows accurate, interpretable and fast classification
Data Mining and Knowledge Discovery
Calibrated lazy associative classification
Information Sciences: an International Journal
An approach for adaptive associative classification
Expert Systems with Applications: An International Journal
Spam detection using web page content: a new battleground
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
Cost-effective on-demand associative author name disambiguation
Information Processing and Management: an International Journal
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
Active associative sampling for author name disambiguation
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
In praise of laziness: a lazy strategy for web information extraction
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
I-prune: Item selection for associative classification
International Journal of Intelligent Systems
Methodology for fraud detection in electronic transactions
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Named entity disambiguation in streaming data
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Automatic vandalism detection in wikipedia with active associative classification
TPDL'12 Proceedings of the Second international conference on Theory and Practice of Digital Libraries
Automatic vandalism detection in wikipedia with active associative classification
TPDL'12 Proceedings of the Second international conference on Theory and Practice of Digital Libraries
CAR-Miner: An efficient algorithm for mining class-association rules
Expert Systems with Applications: An International Journal
Interestingness measures for classification based on association rules
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
SpaDeS: Detecting spammers at the source network
Computer Networks: The International Journal of Computer and Telecommunications Networking
Aggregating productivity indices for ranking researchers across multiple areas
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Discovering diverse association rules from multidimensional schema
Expert Systems with Applications: An International Journal
Dengue surveillance based on a computational model of spatio-temporal locality of Twitter
Proceedings of the 3rd International Web Science Conference
CBC: An associative classifier with a small number of rules
Decision Support Systems
Lazy attribute selection: Choosing attributes at classification time
Intelligent Data Analysis
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Decision tree classifiers perform a greedy search for rules by heuristically selecting the most promising features. Such greedy (local) search may discard important rules. Associative classifiers, on the other hand, perform a global search for rules satisfying some quality constraints (i.e., minimum support). This global search, however, may generate a large number of rules. Further, many of these rules may be useless during classification, and worst, important rules may never be mined. Lazy (non-eager) associative classification overcomes this problem by focusing on the features of the given test instance, increasing the chance of generating more rules that are useful for classifying the test instance. In this paper we assess the performance of lazy associative classification. First we demonstrate that an associative classifier performs no worse than the corresponding decision tree classifier. Also we demonstrate that lazy classifiers outperform the corresponding eager ones. Our claims are empirically confirmed by an extensive set of experimental results. We show that our proposed lazy associative classifier is responsible for an error rate reduction of approximately 10% when compared against its eager counterpart, and for a reduction of 20% when compared against a decision tree classifier. A simple caching mechanism makes lazy associative classification fast, and thus improvements in the execution time are also observed.