Decision Tree Algorithms
Evolutionary design of decision-tree algorithms tailored to microarray gene expression data sets
... induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive ...inducing decision trees is the greedy ...
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Assessment of Decision Tree Algorithms on Student’s Recital
... dataset. Decision tree method is commonly used in Classification ...technique. Decision tree model is represented by branch and ...several Decision tree algorithms to ...
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Performance Analysis of Undergraduate Students Placement Selection using Decision Tree Algorithms
... use Decision tree algorithms to predict student selection in ...different Decision tree algorithms used to predict student performance in ...step Decision tree ...
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Title: Popular Decision Tree Algorithms of Data Mining Techniques: A Review
... Abstract— The technologies of data production and collection have been advanced rapidly. As result to that, everything gets automatically: data storage and accumulation. Data mining is the tool to predict the unobserved ...
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Applying Association Rules and Decision Tree Algorithms with Tumor Diagnosis Data
... and decision tree are interested mining algorithms which can be used to find and explore relations between attributes in a data ...and decision tree algorithms are applied with ...
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Student’s Performance Analysis using Decision Tree Algorithms
... including decision tree method. In this work, decision trees were used which include BFTree, J48 and ...different Decision tree algorithms ...
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A comparative study of reduced error pruning method in decision tree algorithms
... IV. E XPERIMENT , R ESULT AND DISCUSSION In this section, we conducted an experiment using Weka application. Weka is a comprehensive suite of Java class libraries that perform many advanced machine learning and data ...
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Performance Evaluation System for Decision Tree Algorithms
... the algorithms date back in the ...history, decision tree algorithms tend to automate the entire process of hypothesis generation and then validation much more completely and in a much more ...
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Comparative Analysis of Spatial Decision Tree Algorithms for Burned Area of Peatland in Rokan Hilir Riau
... spatial decision tree algorithms to classify classes before burned, burned and after burned from remote sensed data of peatland area in Kubu and Pasir Limau Kapas subdistrict, Rokan Hilir, ...The ...
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ANALYZING EMPLOYEE ATTRITION USING DECISION TREE ALGORITHMS
... (decision tree algorithms) were used to generate rule-sets that can be used to help recognize employee’s with high probability of attrition in the nearest ...
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Implementation of Artificial Neural Networks and Decision Tree Algorithms for Heart Disease Diagnosis
... Table 7 shows that the accuracy of Zero R was (55.55%), the accuracy of MLP was (99.52%), and the accuracy of J48 was (100%). Zero R took 0.2 seconds to build the model, MLP took 0.57 seconds to build the model, and J48 ...
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A COMPARATIVE STUDY OF DECISION TREE ALGORITHMS FOR CLASS IMBALANCED LEARNING IN CREDIT CARD FRAUD DETECTION
... C4.5 tree in ...unpruned tree. C4.5 is an algorithm that is used to create a decision tree developed by Quinlan ...The decision trees generated by ...The decision tree ...
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Decision Tree Algorithms for Diagnosis of Cardiac Disease Treatment
... Decision tree learning has main ...understand. Decision tree is same as tree ...the tree is the root node. Every hub in the tree indicates a test on some quality and each ...
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Performance Analysis of Decision Tree Algorithms on Mushroom Dataset
... namely decision tree classification has the task to predict accurately the class to which the data samples belong ...of decision tree classification using ID3, CART, and HT are ...
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Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data
... generating decision-tree algorithms are effective for the problem of RDD with flexible-receptor docking ...a tree takes O(m × n log n) time (m is the number of attributes and n the number of ...
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Selecting a representative decision tree from an ensemble of decision-tree models for fast big data classification
... of tree ensembles by choosing a single representative model out of ensemble of multiple decision-tree ...this tree selection methodology to a popular ensemble algorithm (majority voting) and ...
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II. DECISION TREE CLASSIFICATION
... the decision tree construction algorithm receives the training data set as an input and constructs the decision tree as an output without any interaction with the ...a decision ...
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Decision Tree Ensemble Selection
... Ensemble models are well-known in machine learning for their accuracy. Their main quality, convergence towards an asymptotic upper limit as the number of internal models increases, is however partly counterbalanced by ...
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Effective Decision Tree Learning
... effective decision tree (EDT) construction algorithm that uses a new error adjusting technique (NEAT) in constructing more accurate decision tree ...in decision tree ...
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Decision Tree Learning for Drools
... each decision tree classifying the given main ...smaller decision trees (less number of Alpha and Terminal nodes) with all object except the complicated case, ...complex decision tree ...
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