Previously we proposed a scheme to generate fuzzy rule-based multiclassification systems by means of bagging, mutual information-based feature selection, and a multicriteria genetic algorithm (GA) for static component classifier selection guided by the ensemble training error. In the current contribution we extend the latter component by the use of two bi-criteria fitness functions, combining t...

Deoxyribo Nucleic Acid (DNA) microarray technology allows us to generate thousands of gene expression in a single chip. Analyzing gene expression data plays vital role in understanding diseases and discovering medicines. Classification of cancer based on gene expression data is a promising research area in the field of bioinformatics and data mining. All genes do not contribute for efficient cl...

Natural human-computer interaction requires, in addition to understand what the speaker is saying, recognition of behavioral descriptors, such as speaker’s personality traits (SPTs). The complexity of this problem depends on the high variability and dimensionality of the acoustic, lexical and situational context manifestations of the SPTs. In this paper, we present a comparative study of automa...

In this paper, we present Deep Extreme Feature Extraction (DEFE), a new ensemble MVA method for searching ττ channel of Higgs bosons in high energy physics. DEFE can be viewed as a deep ensemble learning scheme that trains a strongly diverse set of neural feature learners without explicitly encouraging diversity and penalizing correlations. This is achieved by adopting an implicit neural contro...

BACKGROUND The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. NEW METHOD We proposed to use an ensemble of suppor...

Basic question arises when classification came in picture classification accuracy, ensemble size, and computational complexity. Feature selection is importance for improvement and performance of classification algorithm. Classification algorithm may not scale up to the size of the full feature set either in sample or time but with feature selection help us to better understand the domain with C...

Detection of mild laryngeal disorders using acoustic parameters of human voice is the main objective in this study. Observations of sustained phonation (audio recordings of vocalized /a/) are labeled by clinical diagnosis and rated by severity (from 0 to 3). Research is exclusively constrained to healthy (severity 0) and mildly pathological (severity 1) cases – two the most difficult classes to...

Different data classification algorithms have been developed and applied in various areas to analyze and extract valuable information and patterns from large datasets with noise and missing values. However, none of them could consistently perform well over all datasets. To this end, ensemble methods have been suggested as the promising measures. This paper proposes a novel hybrid algorithm, whi...

In building a response model, determining the inputs to the model has been an important issue because of the complexities of the marketing problem and limitations of mental models for decision-making. It is common that the customers’ historical purchase data contains many irrelevant or redundant features thus result in bad model performance. Furthermore, single complex models based on feature s...

Ensemble has been proved a successful approach for enhancing the performance of a single classifier. But there are two key factors directly influencing the outcomes of an ensemble: accuracy of each single member and diversity between the members. There have been many approaches used in the literature to create the mentioned diversity. In this paper, we add to them a novel approach, in which cla...