decision tree induction
On the automatic design of decision-tree induction algorithms
... Designing Decision-Tree induction algorithms, which is the main topic of the defended ...in decision-tree ...design decision- tree induction ...that ...
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Clus-DTI: improving decision-tree classification with a clustering-based decision-tree induction algorithm
... Abstract Decision-tree induction is a well-known tech- nique for assigning objects to categories in a white-box fash- ...Most decision-tree induction algorithms rely on a sub- ...
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A survey of cost sensitive decision tree induction algorithms
... Cost-Sensitive Decision Tree Induction Algorithms SUSAN LOMAX, University of Salford SUNIL VADERA, University of Salford The past decade has seen a significant interest on the problem of inducing ...
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Generalization and Decision Tree Induction: Efficient Classification in Data Mining
... 4. High-level generalization: The training data are gen- eralized to a high concept level before decision tree induction. We call such an algorithm HiGen. Since the generalized data induced in HiGen ...
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VPRSM based Improved Decision Tree Induction Approach for HandlingUncertain Data
... intelligent decision tree ...in Decision Tree(DT) classification handles uncertainties in the training data and results in generation of more precise decision ...DT induction ...
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Automatic Partial Parsing Rule Acquisition Using Decision Tree Induction
... a tree-annotated corpus using a decision tree ...using decision tree induction, with features of contextual and lexical information for the classification; and (3) verifying ...
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Title: USING DECISION TREE INDUCTION TO PREDICT THE TYPE OF EATING DISORDER
... Classification, Decision tree induction, prediction, frequent item set mining, association rule mining ...
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Estimating the class prior in positive and unlabeled data through decision tree induction
... [email protected] Abstract For tasks such as medical diagnosis and knowledge base com- pletion, a classifier may only have access to positive and un- labeled examples, where the unlabeled data consists of ...
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Cybercrime Profiling: Decision Tree Induction, Examining Perceptions of Internet Risk and Cybercrime Victimisation
... is decision-tree induction, as this offers a clear framework for learning and reasoning from feature-based ...examples. Decision-trees help ascertain which course of action to take or ...
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Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data
... specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, ...
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Mining Fuzzy Multidimensional Association Rules Using Fuzzy Decision Tree Induction Approach
... Decision Tree Induction (DTI) has been used in machine learning and in data mining as a model for prediction a target value based on a given relational ...commercial decision tree ...
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Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm
... a decision tree induction algo- ...the decision tree, including both the costs of tests (features, measurements) and the costs of classification ...
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Cybercrime Profiling: Decision-Tree Induction, Examining Perceptions of Internet Risk and Cybercrime Victimisation
... is decision-tree induction, as this offers a clear framework for learning and reasoning from feature-based ...examples. Decision-trees help ascertain which course of action to take or ...
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A ROBUST APPROACH FOR DATA CLEANING USED BY DECISION TREE INDUCTION METHOD IN THE ENTERPRISE DATA WAREHOUSE
... in decision support ...The Decision Tree Induction Algorithm is used to fill the Missing Values in different data sources and also provided solutions to clean Dummy Values, Cryptic Values, and ...
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Feature Selection Using Decision Tree Induction in Class level Metrics Dataset for Software Defect Predictions
... three decision tree algorithms given for classification for which the classification is done using decision tree induction and trees are constructed by rule generation using the input ...
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Decision Tree Induction: An Example
... Information gain measure is biased towards attributes with a large number of values.. C4.5 (a successor of ID3) uses gain ratio to overcome the problem (normalization to.[r] ...
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Evolutionary Algorithms in Decision Tree Induction
... life decision- making situations force people to choose over a set of possible alternatives with the aim of maximizing some utility ...the decision-making process has to face with the evaluation of a ...
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Hyper-heuristic decision tree induction
... Chapter 3 - Hyper-heuristic Rules using Partition Size 62 way of choosing a splitting attribute from that same partition. One reason for this could be that this correlation is too weak. It might well be that the size of ...
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Decision Tree Induction in High Dimensional, Hierarchically Distributed Databases
... on decision trees is one of the impor- tant problems in data mining and has applications in many ...inducing decision trees in a large distributed network of high dimensional ...Current decision ...
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Optimal constraint-based decision tree induction from itemset lattices
... a tree; ii) an exact algorithm can indeed give significantly better results than a heuristic learner if the optimisation criterion is well- defined; iii) exact results allow to study the behavior of the trees with ...
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