Decision tree induction has been widely used to generate classifiers from training data through a process of recursively splitting the data space. In the case of training on continuous-valued data, the associated attributes must be discretized in advance or during the learning process. The commonly used method is to partition the attribute range into two or several intervals using a single or a...

A decision tree is an effective means of data classification from which one can obtain rules that are easy to understand. However, decision trees cannot be conventionally constructed for data which are not explicitly expressed with attribute-value pairs such as graph-structured data. We have proposed a novel algorithm, named Chunkingless Graph-Based Induction (Cl-GBI), for extracting typical pa...

Developing a predictive model for forest fires occurrence is an important activity in a fire prevention program. The model describes characteristics of areas where fires occur based on past fires data. It is essential as an early warning system for preventing forest fires, thus major damages because of fires can be avoided. This study describes the application of data mining technique namely de...

This paper proposed a method using principle component analysis based on graph entropy (PCA-GE) and J48 decision tree on electroencephalogram (EEG) signals to predict whether a person is alcoholic or not. Analysis is performed in two stages: feature extraction and classification. The principle component analysis (PCA) chooses the optimal subset of channels based on graph entropy technique and t...

This paper compares the performance of logistic regression to decision-tree induction in classifying patients as having acute cardiac ischemia. This comparison was performed using the database of 5773 patients originally used to develop the logistic-regression tool and test it prospectively. Both the ability to classify cases and the ability to estimate the probability of ischemia were compared...

In order to improve the recognition accuracy of speech emotion recognition, in this paper, a novel hierarchical method based on improved Decision Directed Acyclic Graph SVM (improved DDAGSVM) is proposed for speech emotion recognition. The improved DDAGSVM is constructed according to the confusion degrees of emotion pairs. In addition, a geodesic distance-based testing algorithm is proposed for...

I of decision trees is a popular and effective method for solving classification problems in data-mining applications. This paper presents a new algorithm for multi-category decision tree induction based on nonlinear programming. This algorithm, termed OC-SEP (Oblique Category SEParation), combines the advantages of several other methods and shows improved generalization performance on a collec...