The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applicatio...

A few years ago a new classifier ensemble method, called rotation forest, was devised. The technique applies Principal Component Analysis to rotate the original feature axes in order to obtain different training sets for learning base classifiers. In the paper we report the results of the investigation aimed to compare the predictive performance of rotation forest with random forest models, bag...

We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearestneighbors, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regre...

In the paper, we present an empirical evaluation of five feature selection methods: ReliefF, random forest feature selector, sequential forward selection, sequential backward selection, and Gini index. Among the evaluated methods, the random forest feature selector has not yet been widely compared to the other methods. In our evaluation, we test how the implemented feature selection can affect ...

Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative fea...

Data analysis domain dealing with data exploration, clustering and classification is an important problem in many experiments of astrophysics, computer vision, bioinformatics etc. The field of machine learning is increasingly becoming popular for performing these tasks. In this thesis we deal with machine learning models based on unsupervised and supervised learning algorithms. In unsupervised ...

Many approaches have been proposed using Electroencephalogram (EEG) to detect epilepsy seizures in their early stages. Epilepsy seizure is a severe neurological disease. Practitioners continue rely on manual testing of EEG signals. Artificial intelligence (AI) and Machine Learning (ML) can effectively deal with this problem. ML be used classify signals employing feature extraction techniques. T...

In this paper I present an extended implementation of the Random ferns algorithm contained in the R package rFerns. It di ers from the original by the ability of consuming categorical and numerical attributes instead of only binary ones. Also, instead of using simple attribute subspace ensemble it employs bagging and thus produce error approximation and variable importance measure modelled afte...

Machine learning is a concerned with the design and development of algorithms. Machine learning is a programming approach to computers to achieve optimization .Classification is the prediction approach in data mining techniques. Decision tree algorithm is the most common classifier to build tree because of it is easier to implement and understand. Attribute selection is a concept by which we wa...

Data Mining is taking out of hidden patterns from huge database. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Nowadays traffic accidents are the major causes of death and injuries i...