Parallel decision tree learning is an effective and efficient approach to scaling the decision tree to large data mining application. Aiming at large scale decision tree learning, we present a novel parallel decision tree learning algorithm in MapReduce framework, called PDTSSE (Parallel Decision Tree via Sampling Splitting points with Estimation). We first propose an estimation method for samp...

The Prism family of algorithms induces modular classification rules which, in contrast to decision tree induction algorithms, do not necessarily fit together into a decision tree structure. Classifiers induced by Prism algorithms achieve a comparable accuracy compared with decision trees and in some cases even outperform decision trees. Both kinds of algorithms tend to overfit on large and nois...

Classiication{rule{learning task is presented as a search process of nding a classiication{ decision tree that meets users' preferences and requirements. Users can control the eeciency of the mining process and the quality of the nal decision tree through the search parameters. This search framework allows users to easily adapt to diierent domains and diierent sets of data by modifying diierent...

Decision tree learning is an important field of machine learning. In this study we examine both formal and practical aspects of decision tree learning. We aim at answering to two important needs: The need for better motivated decision tree learners and an environment facilitating experimentation with inductive learning algorithms. As results we obtain new practical tools and useful techniques f...

This paper proposes a combinatorial method based on different clustering algorithms with ID3 decision tree classification for the classification of network anomaly detection. The idea is to detect the network anomalies by first applying any clustering algorithm to partition it into a number of clusters and then applying ID3 algorithm for the decision that whether an anomaly has been detected or...

This paper details the application of a genetic programming framework for classification of decision tree of Soil data to classify soil texture. The database contains measurements of soil profile data. We have applied GATree for generating classification decision tree. GATree is a decision tree builder that is based on Genetic Algorithms (GAs). The idea behind it is rather simple but powerful. ...

OBJECTIVE The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. T...

Intrusion detection technology exists a lot of problems, such as low performance, low intelligent level, high false alarm rate, high false negative rate and so on. There is a need to develop some robust decision tree in order to produce effective decision rules from the attacked data. In this paper, ID3 decision tree classification method is used to build an effective decision tree for intrusio...