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Abstract: In this paper we present a new multivariate decision tree algorithm LMDT, which combines linear machines with decision trees. LMDT constructs each test in a decision tree by training a linear machine and then eliminating irrelevant and noisy variables in a controlled manner. To examine LMDT's ability to find good generalizations we present results for a variety of domains. We compare LMDT empirically to a univariate decision tree algorithm and observe that when multivariate tests are the... (Update)
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...the accuracy of classifiers. Many studies have been performed to determine which algorithm has the highest prediction accuracy [SMT91, BU92, DBP93, CM94, MST94] These studies indicate that no algorithm is uniformly most accurate over all the datasets studied. Mehta et al....
...tree learning. Decision trees built by selective decision tree learning algorithms such as C4.5 are called univariate trees [Brodley and Utgoff, 1992] since a test at each decision node is based on a single primitive attribute. By contrast, constructive decision tree...
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BibTeX entry: (Update)
Brodley, C. E., & Utgoff, P. E. (1992). Multivariate versus univariate decision trees. Tech. http://citeseer.ist.psu.edu/brodley92multivariate.html More
@techreport{ brodley92multivariate,
author = "Carla E. Brodley and Paul E. Utgoff",
title = "Multivariate Versus Univariate Decision Trees",
number = "UM-CS-1992-008",
month = ",",
year = "1992",
url = "citeseer.ist.psu.edu/brodley92multivariate.html" }
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