Abstract The progressive reduction of dopaminergic neurons in the human brain, especially at substantia nigra is one principal causes Parkinson’s Disease (PD). Voice alteration earliest symptoms found PD patients. Therefore, impaired subjects’ acoustic voice signal plays a crucial role detecting presence Parkinson's. This manuscript presents four distinct decision tree ensemble methods detectio...

Results of damage prediction in buildings can be used as a useful tool for managing and decreasing seismic risk of earthquakes. In this study, damage spectrum and C4.5 decision tree algorithm were utilized for damage prediction in steel buildings during earthquakes. In order to prepare the damage spectrum, steel buildings were modeled as a single-degree-of-freedom (SDOF) system and time-history...

This article introduces a novel approach for building heterogeneous ensembles based on genetic programming (GP). Ensemble learning is a paradigm that aims at combining individual classifiers outputs to improve their performance. Commonly, classifiers outputs are combined by a weighted sum or a voting strategy. However, linear fusion functions may not effectively exploit individual models’ redun...

The capability to perform anomaly detection in a resource-constrained setting, such as an edge device or loaded server, is of increasing need due emerging on-premises computation constraints well security, privacy and profitability reasons. Yet, the size datasets often results current methods being too resource consuming, particular decision-tree based ensemble classifiers. To address this need...

A new boosting algorithm of Freund and Schapire is used to improve the performance of decision trees which are constructed usin: the information ratio criterion of Quinlan’s C4.5 algorithm. This boosting algorithm iteratively constructs a series of decision tress, each decision tree being trained and pruned on examples that have been filtered by previously trained trees. Examples that have been...

Ensemble methods take their output from a set of base predictors. The ensemble accuracy depends on two factors: the base classifiers accuracy and their diversity (how different are these base classifiers outputs from each other). An approach for increasing the diversity of the base classifiers is presented in this paper. The method builds some new features to be added to the base classifier tra...

Classification models for the automatic detection of abnormalities on histological samples do exists, with an active debate on the cost associated with false negative diagnosis (underdiagnosis) and false positive diagnosis (overdiagnosis). Current models tend to underdiagnose, failing to recognize a potentially fatal disease. The objective of this study is to investigate the possibility of auto...

Data mining is the process of analyzing large quantities of data and summarizing it into useful information. In medical diagnoses the role of data mining approaches increasing rapidly. Particularly Classification algorithms are very helpful in classifying the data, which is important in decision making process for medical practitioners. Further to enhance the classifier accuracy various pre-pro...