Computer aided detection via asymmetric cascade of sparse hyperplane classifiers
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Metadata and its impact on libraries: Book Reviews
Journal of the American Society for Information Science and Technology
Instance weighting versus threshold adjusting for cost-sensitive classification
Knowledge and Information Systems
Data & Knowledge Engineering
Incorporating domain knowledge into data mining classifiers: An application in indirect lending
Decision Support Systems
Identifying user preferences with Wrapper-based Decision Trees
Expert Systems with Applications: An International Journal
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While decision tree techniques have been widely used in classification applications, a shortcoming of many decision tree inducers is that they do not learn intermediate concepts, i.e., at each node, only one of the original features is involved in the branching decision. Combining other classification methods, which learn intermediate concepts, with decision tree inducers can produce more flexible decision boundaries that separate different classes, potentially improving classification accuracy. We propose a generic algorithm for cascade generalization of decision tree inducers with the maximum cascading depth as a parameter to constrain the degree of cascading. Cascading methods proposed in the past, i.e., loose coupling and tight coupling, are strictly special cases of this new algorithm. We have empirically evaluated the proposed algorithm using logistic regression and C4.5 as base inducers on 32 UCI data sets and found that neither loose coupling nor tight coupling is always the best cascading strategy and that the maximum cascading depth in the proposed algorithm can be tuned for better classification accuracy. We have also empirically compared the proposed algorithm and ensemble methods such as bagging and boosting and found that the proposed algorithm performs marginally better than bagging and boosting on the average.