A hyperspectral image is used in remote sensing to identify different type of coverts on the Earth surface. It is composed of pixels and each pixel consist of spectral bands of the electromagnetic reflected spectrum. Neural networks and ensemble techniques have been applied to remote sensing images with a low number of spectral bands per pixel (less than 20). In this paper we apply different en...

For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In t...

In order to lower the classification cost and improve the performance of the classifier, this paper proposes the approach of the dynamic cost-sensitive ensemble classification based on extreme learning machine for imbalanced massive data streams (DCECIMDS). Firstly, this paper gives the method of concept drifts detection by extracting the attributive characters of imbalanced massive data stream...

The objective of this study is to develop an ensemble classifier with Merit Merge feature selection that will enhance efficiency of classification in a multivariate multiclass medical data for effective disease diagnostics. The large volumes of features extracted from brain Magnetic Resonance Images and neuropsychological tests for diagnosis lead to more complexity in classification procedures....

Multi-sensor systems (MSS) have been increasingly applied in pattern classification while searching for the optimal classification framework is still an open problem. The development of the classifier ensemble seems to provide a promising solution. The classifier ensemble is a learning paradigm where many classifiers are jointly used to solve a problem, which has been proven an effective method...

OBJECT The classification of cancer based on gene expression data is one of the most important procedures in bioinformatics. In order to obtain highly accurate results, ensemble approaches have been applied when classifying DNA microarray data. Diversity is very important in these ensemble approaches, but it is difficult to apply conventional diversity measures when there are only a few trainin...

Kernel-based methods and ensemble learning are two important paradigms for the classification of hyperspectral remote sensing images. However, they were developed in parallel with different principles. In this paper, we aim to combine the advantages of kernel and ensemble methods by proposing a kernel supervised ensemble classification method. In particular, the proposed method, namely RoF-KOPL...

Research on classifying high dimensional datasets is an open direction in the pattern recognition yet. High dimensional feature spaces cause scalability problems for machine learning algorithms because the complexity of a high dimensional space increases exponentially with the number of features. Recently a number of ensemble techniques using different classifiers have proposed for classifying ...

In recent times, the manufacturing processes are faced with many external or internal (the increase of customized product re-scheduling, process reliability,.. ) changes. Therefore, monitoring and quality management activities for these manufacturing processes are difficult. Thus, the managers need more proactive approaches to deal with this variability. In this study, a proactive quality monit...

Instead of traditional (nominal) classification we investigate the subject of ordinal classification or ranking. An enhanced method based on an ensemble of Support Vector Machines (SVM’s) is proposed. Each binary classifier is trained with specific weights for each object in the training data set. Experiments on benchmark datasets and synthetic data indicate that the performance of our approach...