نتایج جستجو برای: decision tree algorithms
تعداد نتایج: 785727 فیلتر نتایج به سال:
We report on work towards flexible algorithms for solving decision problems represented as influence diagrams. An algorithm is given to construct a tree structure for each decision node in an influence diagram. Each tree represents a decision function and is constructed incrementally. The improvements to the tree converge to the optimal decision function (neglecting computational costs) and the...
Typical data mining algorithms follow a so called “black-box” paradigm, where the logic is hidden from the user not to overburden him. We show that “white-box” algorithms constructed with reusable components design can have significant benefits for researchers, and end users as well. We developed a component-based algorithm design platform, and used it for “white-box” algorithm construction. Th...
Data mining is the useful tool to discovering the knowledge from large data. Different methods & algorithms are available in data mining. Classification is most common method used for finding the mine rule from the large database. Decision tree method generally used for the Classification, because it is the simple hierarchical structure for the user understanding & decision making. Various data...
In Data mining applications, very large training data sets with several million records are common. Decision trees are very much powerful and excellent technique for both classification and prediction problems. Many decision tree construction algorithms have been proposed to develop and handle large or small training data. Some related algorithms are best for large data sets and some for small ...
Introduction: Since the delay or mistake in the diagnosis of mood disorders due to the similarity of their symptoms hinders effective treatment, this study aimed to accurately diagnose mood disorders including psychosis, autism, personality disorder, bipolar, depression, and schizophrenia, through modeling and analyzing patients' data. Method: Data collected in this applied developmental resear...
We study the decision tree complexity of the discrete Fréchet distance (decision version) under the L1 and L∞ metrics over R. While algorithms for the Euclidean (L2) discrete Fréchet distance were studied extensively, the problem in other metrics such as L1 and L∞ seems to be much less investigated. For the L1 discrete Fréchet distance in R we present a 2d-linear decision tree with depth O(n lo...
For the sustainable use of groundwater, this study analyzed groundwater productivity-potential using a decision-tree approach in a geographic information system (GIS) in Boryeong and Pohang cities, Korea. The model was based on the relationship between groundwater-productivity data, including specific capacity (SPC), and its related hydrogeological factors. SPC data which is measured and calcul...
The decision tree model has gained great popularity both in academia and industry due to its capability of learning highly non-linear decision boundaries, and at the same time, still preserving interpretability that usually translates into transparency of decision-making. However, it has been a longstanding challenge for learning robust decision tree models since the learning process is usually...
Prediction of student graduation accuracy using decision tree with application of genetic algorithms
We present a self-adjusting point location structure for convex subdivisions. Let n be the number of vertices in a convex subdivision S. Our structure for S uses O(n) space and processes any online query sequence σ in O(n+ OPT) time, where OPT is the minimum time required by any linear decision tree for answering point location queries in S to process σ. The O(n + OPT) time bound includes the p...
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