Its workbench interface is used in both industry and academia, and it provides a way for users to quickly and easily use and assess various methods of machine learning algorithms with training sets of data. The goal of this project is to experiment with the WEKA J48 decision tree algorithm ...
This paper compares the two famous algorithms called Bayesian and Decision tree algorithm and how it works on nominal and numerical data sets and demonstrates its results. The accuracy, precision, and classification errors are also measured to compare algorithm. WEKA tool has been used to perform ...
This tutorial explains WEKA Dataset, Classifier, and J48 Algorithm for Decision Tree. Also provides information about sample ARFF datasets for Weka: In theprevious tutorial, we learned about the Weka Machine Learning tool, its features, and how to download, install, and use Weka Machine Learning ...
A supervised classification technique, C4.5 decision tree (called J48 in WEKA software), was used since it is based on a set of classes known a priori (i.e., in this study, the sport classes) [38]. This technique is an algorithm used to generate a decision tree, which begins with a...
The classification base model tree was created by the C4.5 algorithm [23] (implemented in Weka by the classifier weka.classifiers.trees.J48). The boosting method used was the meta learning AdaBoostM1 algorithm, an extension of the AdaBoost to the multiclass case, with more than two possible...
Java implementation of the C4.5 algorithm is known as J48, which is available in WEKA data mining tool. Where: |Dj|/|D| acts as the weight of the jth partition. v is the number of discrete values in attribute A. The gain ratio can be defined as The attribute with the highest gain ...
Java implementation of the C4.5 algorithm is known as J48, which is available in WEKA data mining tool. Where: |Dj|/|D| acts as the weight of the jth partition. v is the number of discrete values in attribute A. The gain ratio can be defined as The attribute with the highest gain ...
A decision tree-based approach for determining low bone mineral density in inflammatory bowel disease using WEKA software. BACKGROUND: Decision tree classification is a standard machine learning technique that has been used for a wide range of applications. Patients with inflam... F Firouzi,M Rashid...
I am not sure what exactly you want but the following code can be useful considering that you saved the decision tree as "tc". 테마복사 CP = tc.CutPoint; NC = tc.NodeClass; for ii = 1:size(CP,1) if ~isnan(CP(ii)) fprintf('if x%d < %f then node %d elseif x%d ...
the use of ANNs in each non-terminal node increased the number of parameters that had to be defined, i.e., the number of hidden layers, the number of nodes in the layers, learning rate, etc. To avoid this, Sankar and Mammone27proposed the neural tree networks (NTN) method, which use...