Boolean Feature Discovery in Empirical Learning
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Making large-scale support vector machine learning practical
Advances in kernel methods
Mining features for sequence classification
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
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
PlanMine: Predicting Plan Failures Using Sequence Mining
Artificial Intelligence Review - Issues on the application of data mining
Machine Learning
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
LPMiner: An Algorithm for Finding Frequent Itemsets Using Length-Decreasing Support Constraint
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Multivariate Versus Univariate Decision Trees
Multivariate Versus Univariate Decision Trees
Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Discovering Dispatching Rules Using Data Mining
Journal of Scheduling
On Mining Instance-Centric Classification Rules
IEEE Transactions on Knowledge and Data Engineering
Minimizing Information Loss and Preserving Privacy
Management Science
On the strength of hyperclique patterns for text categorization
Information Sciences: an International Journal
Vote prediction by iterative domain knowledge and attribute elimination
International Journal of Business Intelligence and Data Mining
Finding "persistent rules": Combining association and classification results
Expert Systems with Applications: An International Journal
Testing terrorism theory with data mining
International Journal of Data Analysis Techniques and Strategies
| Hi-index | 0.00 |
Advances in the efficient discovery of frequent itemsets have led to the development of a number of schemes that use frequent itemsets to aid developing accurate and efficient classifiers. These approaches use the frequent itemsets to generate a set of composite features that expand the dimensionality of the underlying dataset. In this paper, we build upon this work and (i) present a variety of schemes for composite feature selection that achieve a substantial reduction in the number of features without adversely affecting the accuracy gains, and (ii) show (both analytically and experimentally) that the composite features can lead to improved classification models even in the context of support vector machines, in which the dimensionality can automatically be expanded by the use of appropriate kernel functions.