نتایج جستجو برای: improved decision tree
تعداد نتایج: 918656 فیلتر نتایج به سال:
In this paper, we present a new learning algorithm for anomaly based network intrusion detection using improved self adaptive naïve Bayesian tree (NBTree), which induces a hybrid of decision tree and naïve Bayesian classifier. The proposed approach scales up the balance detections for different attack types and keeps the false positives at acceptable level in intrusion detection. In complex and...
The real estimation of the volume of sediments carried by rivers in water projects is very important. In fact, achieving the most important ways to calculate sediment discharge has been considered as the objective of the most research projects. Among these methods, the machine learning methods such as decision trees model (that are based on the principles of learning) can be presented. Decision...
Background: Today, in most hospitals in Iran, there is an extensive database of patient characteristics that includes a large amount of information related to medical, family and medical records. Finding a knowledge model of this information can help to predict the performance of the medical system and improve educational processes. Methods: Data mining techniques are analytical tools that are...
Introduction: Autism is a nervous system disorder, and since there is no direct diagnosis for it, data mining can help diagnose the disease. Ontology as a backbone of the semantic web, a knowledge database with shareability and reusability, can be a confirmation of the correctness of disease diagnosis systems. This study aimed to provide a system for diagnosing autistic children with a combinat...
in this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. then, for optimizing the initial feature vector, a genetic algorithm is proposed. some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and k-nearest neig...
In this paper, we present a new phrase break prediction architecture that integrates probabilistic approach with decision-tree based error correction. The probabilistic method alone usually su ers from performance degradation due to inherent data sparseness problems and it only covers a limited range of contextual information. Moreover, the module can not utilize the selective morpheme tag and ...
— 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. In this paper, the shortcoming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm which is improved version of ID3....
Key resource page is one of the most important search target pages for Web search users. Decision tree learning is one of the most widely-used and practical methods for inductive inference in machine learning. Because of the difficulty in uniform sampling of Web pages, there are not enough negative instances for training a key resource decision tree. To solve the problem, the original algorithm...
Battery state of health (SOH) estimation is crucial for the remaining driving range electric vehicles and one core functions battery management system (BMS). The lithium feature sample data used in this paper extracted from National Aeronautics Space Administration (NASA) United States. Based on obtained samples, a decision tree algorithm to analyze them obtain importance each feature. Five gro...
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