Fusion and ensemble is important technique of machine learning. Fusion fused the feature attribute of different classifier and improved the classification of binary classifier. Instead of that ensemble technique provide the facility of merge two individual classifier and improve the performance of both classifiers. The ensemble technique of classifier depends on number of nearer point of classi...

The nature of data streams requires classification algorithms to be real-time, efficient, and able to cope with high-dimensional data that are continuously arriving. It is a known fact that in high-dimensional datasets, not all features are critical for training a classifier. To improve the performance of data stream classification, we propose an algorithm called HEFT-Stream (Heterogeneous Ense...

Crime forecasting is notoriously difficult. A crime incident is a multi-dimensional complex phenomenon that is closely associated with temporal, spatial, societal, and ecological factors. In an attempt to utilize all these factors in crime pattern formulation, we propose a new feature construction and feature selection framework for crime forecasting. A new concept of multi-dimensional feature ...

Bayesian Model averaging is a natural response to model uncertainty. It has become an important practical tool for dealing with model uncertainty, in particular in empirical settings with large numbers of potential models and relatively limited numbers of observations. Most of this paper focuses on the problem of variable selection in normal linear regression models, but also briefly considers ...

Omics data such as microarray transcriptomic and mass spectrometry proteomic data are typically characterized by high dimensionality and relatively small sample sizes. In order to discover biomarkers for diagnosis and prognosis from omics data, feature selection has become an indispensable step to find a parsimonious set of informative features. However, many previous studies report considerabl...

This paper addresses Wrapper-like approaches to feature subset selection and the production of classifier ensembles based on members with different feature subsets. The paper starts with the observation that if an insufficient amount of data is used to guide the Wrapper search then the feature selection will overfit the data. If the objective of the feature selection exercise is to build a bett...

Recent research has proved the benefits of the use of ensembles of classifiers for classification problems. Ensembles of classifiers can be constructed by a number of methods manipulating the training set with the purpose of creating a set of diverse and accurate base classifiers. One way to manipulate the training set for construction of the base classifiers is to apply feature selection. In t...

In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and K-nearest neig...

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Automatic annotation of music files is a key problem in multimedia information retrieval. In this paper we present a solution to this problem that addresses the issues of feature extraction, feature selection and design of classifier. We outline a process for feature extraction based on the discrete wavelet packet transform and we evaluate a variety of wrapper-based feature subset selection str...