On using Bayesian networks for complexity reduction in decision trees
نویسندگان
چکیده
منابع مشابه
On using Bayesian networks for complexity reduction in decision trees
In this paper we use the Bayesian network as a tool of explorative analysis: its theory guarantees that, given the structure and some assumptions, the Markov blanket of a variable is the minimal conditioning set through which the variable is independent from all the others. We use the Markov blanket of a target variable to extract the relevant features for constructing a decision tree (DT). Our...
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چکیده رساله/پایان نامه : تاکنون روشهای متعددی در ارتباط با مکان یابی خطا در شبکه انتقال ارائه شده است. استفاده مستقیم از این روشها در شبکه توزیع به دلایلی همچون وجود انشعابهای متعدد، غیر یکنواختی فیدرها (خطوط کابلی، خطوط هوایی، سطح مقطع متفاوت انشعاب ها و تنه اصلی فیدر)، نامتعادلی (عدم جابجا شدگی خطوط، بارهای تکفاز و سه فاز)، ثابت نبودن بار و اندازه گیری مقادیر ولتاژ و جریان فقط در ابتدای...
Feature Selection for the Naive Bayesian Classifier Using Decision Trees
It is known that Naïve Bayesian classifier (NB) works very well on some domains, and poorly on some. The performance of NB suffers in domains that involve correlated features. C4.5 decision trees, on the other hand, typically perform better than the Naïve Bayesian a lgorithm on such domains. This paper describes a Selective Bayesian classifier (SBC) that simply uses only those features that C4....
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Various factors a ecting decision tree learning time are explored. The factors which consistently a ect accuracy are those which directly or indirectly (as in the handling of continuous attributes) allow a greater variety of potential trees to be explored. Other factors, e.g., pruning and choice of heuristics, generally have little e ect on accuracy, but signi cantly a ect learning time. We pro...
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ژورنال
عنوان ژورنال: Statistical Methods and Applications
سال: 2009
ISSN: 1618-2510,1613-981X
DOI: 10.1007/s10260-009-0116-1