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: In this paper, a three-step data mining framework is applied to discover occupancy patterns in office spaces, which can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.
239 citations
27 Oct 1986
TL;DR: A randomized variant of alphabeta pruning is analyzed, it is shown that it is considerably faster than the deterministic one in worst case, and it is proved optimal for uniform trees.
Abstract: The Boolean Decision tree model is perhaps the simplest model that computes Boolean functions; it charges only for reading an input variable. We study the power of randomness (vs. both determinism and non-determinism) in this model, and prove separation results between the three complexity measures. These results are obtained via general and efficient methods for computing upper and lower bounds on the probabilistic complexity of evaluating Boolean formulae in which every variable appears exactly once (AND/OR tree with distinct leaves). These bounds are shown to be exactly tight for interesting families of such tree functions. We then apply our results to the complexity of evaluating game trees, which is a central problem in AI. These trees are similar to Boolean tree functions, except that input variables (leaves) may take values from a large set (of valuations to game positions) and the AND/OR nodes are replaced by MIN/MAX nodes. Here the cost is the number of positions (leaves) probed by the algorithm. The best known algorithm for this problem is the alpha-beta pruning method. As a deterministic algorithm, it will in the worst case have to examine all positions. Many papers studied the expected behavior of alpha-beta pruning (on uniform trees) under the unreasonable assumption that position values are drawn independently from some distribution. We analyze a randomized variant of alphabeta pruning, show that it is considerably faster than the deterministic one in worst case, and prove it optimal for uniform trees.
236 citations
16 Oct 1978
TL;DR: It is shown that a decision tree of height O(dn log n) can be constructed to process n operations in d dimensions, suggesting that the standard decision tree model will not provide a useful method for investigating the complexity of orthogonal range queries.
Abstract: Given a set of points in a d-dimensional space, an orthogonal range query is a request for the number of points in a specified d-dimensional box. We present a data structure and algorithm which enable one to insert and delete points and to perform orthogonal range queries. The worstcase time complexity for n operations is O(n logd n); the space usea is O(n logd-1 n). (O-notation here is with respect to n; the constant is allowed to depend on d.) Next we briefly discuss decision tree bounds on the complexity of orthogonal range queries. We show that a decision tree of height O(dn log n) (Where the implied constant does not depend on d or n) can be constructed to process n operations in d dimensions. This suggests that the standard decision tree model will not provide a useful method for investigating the complexity of such problems.
232 citations
TL;DR: The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.
224 citations
Book•
01 Sep 1989
TL;DR: Why Model How People Make Decisions How to Model Simple `Do It Don't Do It' DecisionsHow to Test Decision Models How Not to Do It Common Problems How to Build Multi-Stage Models What's the Payoff
Abstract: Why Model How People Make Decisions How to Model Simple `Do It Don't Do It' Decisions How to Test Decision Models How Not to Do It Common Problems How to Build Multi-Stage Models What's the Payoff
221 citations