The electrocardiogram (ECG) is a non-invasive method to measure and record the electrical activity of the heart. ECG signal analysis has an important role on the diagnosis of heart diseases especially, abnormal or irregular heartbeats, namely arrhythmia. There are three basic waves; P, QRS and T in healthy EGC signal. The detection of these waves and time domain morphological properties represe...

This paper proposes a new ensemble method that constructs an ensemble of tree-structured classifiers using multi-view learning. We are motivated by the fact that an ensemble can outperform its members providing that these classifiers are diverse and accurate. In order to construct diverse individual classifiers, we assume that the object to be classified is described by multiple feature sets (v...

By embedding multiple proximal SVM classifiers into a binary tree architecture, it is possible to turn an arbitrary multi-classes problem into a hierarchy of binary classifications. The critical issue then consists in determining in each node of the tree how to aggregate the multiple classes into a pair of say overlay classes to discriminate. As a fundamental contribution, our paper proposes to...

Given a choice of classifiers each performing differently on different datasets the best option to assume is an ensemble of classifiers. An ensemble uses a single learning algorithm, whereas in this paper we propose a two stage stacking method with decision tree c4.5 as meta classifier. The base classifiers are Naïve Bayes, KNN and C4.5 tree. The decision tree learns from the classification out...

Ensemble methods improve accuracy by combining the predictions of a set of different hypotheses. However, there is an important shortcoming associated with ensemble methods. Huge amounts of memory are required to store a set of multiple hypotheses. In this work we devise an ensemble method that partially solves this drawbacks. The key point is that components share their common parts. For this ...

Ensemble methods are popular learning methods that usually increase the predictive accuracy of a classifier though at the cost of interpretability and insight in the decision process. In this paper we aim to overcome this issue of comprehensibility by learning a single decision tree that approximates an ensemble of decision trees. The new model is obtained by exploiting the class distributions ...

This research presents a new learning model, the Parallel Decision DAG (PDDAG), and shows how to use it to represent an ensemble of decision trees while using significantly less storage. Ensembles such as Bagging and Boosting have a high probability of encoding redundant data structures, and PDDAGs provide a way to remove this redundancy in decision tree based ensembles. When trained by encodin...

When predictive modeling requires comprehensible models, most data miners will use specialized techniques producing rule sets or decision trees. This study, however, shows that genetically evolved decision trees may very well outperform the more specialized techniques. The proposed approach evolves a number of decision trees and then uses one of several suggested selection strategies to pick on...