1,720,982 research outputs found
Discrete support vector decision trees via tabu-search
No full textAn algorithm is proposed for generating decision trees in which multivariate splitting rules are based on the new concept of discrete support vector machines. By this term a discrete version of SVMs is denoted in which the error is properly expressed as the count of misclassified instances, in place of a proxy of the misclassification distance considered by traditional SVMs. The resulting mixed integer programming problem formulated at each node of the decision tree is then efficiently solved by a tabu search heuristic. Computational tests performed on both well-known benchmark and large marketing datasets indicate that the proposed algorithm consistently outperforms other classification approaches in terms of accuracy, and is therefore capable of good generalization on validation sets
Multivariate classification trees based on minimum features discrete support vector machines
No full textA variant of support vector machines is proposed in which the empirical error is expressed as a discrete measure, by counting the number of misclassified instances, and an additional term is considered in order to reduce the complexity of the rule generated. This leads to the formulation of a mixed integer programming problem, solved via a sequential LP-based heuristic. We then devise a procedure for generating decision trees in which a multivariate splitting rule is derived at each node from the approximate solution of the proposed discrete SVM. Computational tests are performed on several benchmark datasets and three large real-world marketing datasets. They indicate that our classifier is more accurate than other well-known methods. It is also empirically shown that discrete SVMs dominate their continuous counterpart when framed within the decision tree algorithm
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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