Topic
Decision tree model
About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.
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TL;DR: Three different types of complexity lower bounds for the one-way unbounded-error and bounded-error error probabilistic communication protocols for boolean functions are proved.
108 citations
TL;DR: It is shown that functional capability of distributed hierarchical multicomponent systems (networks) can be described by the directed rooted tree model according to the above fuzzy graph idea.
105 citations
25 Jul 2009
TL;DR: The experiment results show that the proposed algorithm can overcome ID3's shortcoming effectively and get more reasonable and effective rules.
Abstract: Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. Through illustrating on the basic ideas of decision tree in data mining, in this paper, the shortcoming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm combining ID3 and Association Function(AF) is presented. The experiment results show that the proposed algorithm can overcome ID3's shortcoming effectively and get more reasonable and effective rules
104 citations
11 Jun 2007
TL;DR: A mathematical foundation for the probabilistic tree model is developed and a very large class of queries for which simple variations of querying and updating algorithms from [3] compute the correct answer is identified.
Abstract: In [3], we introduced a framework for querying and updating probabilistic information over unordered labeled trees, the probabilistic tree model. The data model is based on trees where nodes are annotated with conjunctions of probabilistic event variables. We briefly described an implementation and scenarios of usage. We develop here a mathematical foundation for this model. In particular, we present complexity results. We identify a very large class of queries for which simple variations of querying and updating algorithms from [3] compute the correct answer. A main contribution is a full complexity analysis of queries and updates. We also exhibit a decision procedure for the equivalence of probabilistic trees and prove it is in co-RP. Furthermore, we study the issue of removing less probable possible worlds, and that of validating a probabilistic tree against a DTD. We show that these two problems are intractable in the most general case.
104 citations
13 Jun 2010
TL;DR: An algorithm for recovering the globally optimal 2D human figure detection using a loopy graph model by recycling the dynamic programming tables associated with the tree model to look up the tree based lower bound rather than recomputing the lower bound from scratch.
Abstract: This paper presents an algorithm for recovering the globally optimal 2D human figure detection using a loopy graph model. This is computationally challenging because the time complexity scales exponentially in the size of the largest clique in the graph. The proposed algorithm uses Branch and Bound (BB) to search for the globally optimal solution. The algorithm converges rapidly in practice and this is due to a novel method for quickly computing tree based lower bounds. The key idea is to recycle the dynamic programming (DP) tables associated with the tree model to look up the tree based lower bound rather than recomputing the lower bound from scratch. This technique is further sped up using Range Minimum Query data structures to provide O(1) cost for computing the lower bound for most iterations of the BB algorithm. The algorithm is evaluated on the Iterative Parsing dataset and it is shown to run fast empirically.
104 citations