A new algorithm for building decision tree classifiers is proposed. The algorithm is executed in a distributed environment and is especially designed for classifying large datasets and streaming data. It is empirically shown to be as accurate as standard decision tree classifiers, while being scalable to infinite streaming data and multiple processors.

The application of boosting procedures to de cision tree algorithms has been shown to pro duce very accurate classi ers These classi ers are in the form of a majority vote over a number of decision trees Unfortunately these classi ers are often large complex and di cult to interpret This paper describes a new type of classi cation rule the alternat ing decision tree which is a generalization of...

There is growing interest in scaling up the widely-used decision-tree learning algorithms to very large data sets. Although numerous diverse techniques have been proposed, a fast tree-growing algorithm without substantial decrease in accuracy and substantial increase in space complexity is essential. In this paper, we present a novel, fast decision-tree learning algorithm that is based on a con...

The present paper aims at constructing the decision tree for a given database which adopts an improved ID3 decision tree algorithm to implement data mining in order to predict the output. The database is generated using the sampling techniques and the classification algorithm is applied on the samples. The obtained results are compared with experimental results in order to verify the validity a...

Background: Diabetes mellitus has several complications. The Late diagnosis of diabetes in people leads to the spread of complications. Therefore, this study has been done to determine the possibility of predicting diabetes type 2 by using data mining techniques. Methods: This is a descriptive-analytic study that was conducted as a cross-sectional study. The study population included people re...

Since the packet classification algorithm has its great importance and extensive applications, lots of scholars have done tons of researches on the algorithm. This paper presents a new method of a packet classification algorithm based on improved decision tree. In the proposed algorithm, multi-point segmentation and redundancy deletion are adopted to compress the depth of decision tree. A new d...

This paper proposes a new cloud intrusion detection system for detecting the intruders in a traditional hybrid virtualized, cloud environment. The paper introduces an effective feature selection algorithm called Temporal Constraint based on Feature Selection algorithm and also proposes a classification algorithm called hybrid decision tree. This hybrid decision tree has been developed by extend...

Genetic Algorithm-based Quadratic Decision Tree (GA-based QDT) has been applied successfully in various classification problems with non-linear class boundaries. However, the execution time of GA-based QDT is quite long. In this paper, a new version of GA-based QDT, called Genetic Algorithm-based Quadratic Decision Tree with k-D Tree (GA-based QDT with k-D Tree), is proposed. In the proposed al...

The current project aims to model and compare the performance of fake news detectors using machine learning algorithms recognize connected political topics with high accuracy. Decision Tree algorithm Random Forest are two algorithms. methods were developed evaluated on a dataset including 44,000 samples. Implemented each through programs performed ten iterations different scales false feeds fac...