C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Machine Learning
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Construct robust rule sets for classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
A review of associative classification mining
The Knowledge Engineering Review
Compact fuzzy association rule-based classifier
Expert Systems with Applications: An International Journal
A Novel Classification Algorithm Based on Association Rules Mining
Knowledge Acquisition: Approaches, Algorithms and Applications
A classification algorithm that derives weighted sum scores for insight into disease
HIKM '09 Proceedings of the Third Australasian Workshop on Health Informatics and Knowledge Management - Volume 97
A clustering rule-based approach to predictive modeling
Proceedings of the 48th Annual Southeast Regional Conference
Using association rules for better treatment of missing values
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
A Clustering Rule Based Approach for Classification Problems
International Journal of Data Warehousing and Mining
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Rule-based classification systems have been widely used in real world applications because of the easy interpretability of rules. Many traditional rule-based classifiers prefer small rule sets to large rule sets, but small classifiers are sensitive to the missing values in unseen test data. In this paper, we present a larger classifier that is less sensitive to the missing values in unseen test data. We experimentally show that it is more accurate than some benchmark classifies when unseen test data have missing values.