We empirically evaluate several state-of-the-art methods for constructing ensembles of classifiers with stacking and show that they perform (at best) comparably to selecting the best classifier from the ensemble by cross validation. We then propose a new method for stacking, that uses multi-response model trees at the meta-level, and show that it outperforms existing stacking approaches, as wel...

Ensemble learning schemes such as AdaBoost and Bagging enhance the performance of a single classifier by combining predictions from multiple classifiers of the same type. The predictions from an ensemble of diverse classifiers can be combined in related ways, e.g. by voting or simply by selecting the best classifier via cross-validation a technique widely used in machine learning. However, sinc...

Automatic image annotation (AIA) refers to the association of words to whole images which is considered as a promising and effective approach to bridge the semantic gap between low-level visual features and high-level semantic concepts. In this paper, we formulate the task of image annotation as a multi-label multi class semantic image classification problem and propose a simple yet effective m...

In this paper, we propose a new boosting algorithm for distributed databases. The main idea of the proposed method is to utilize the parallelism of the distributed databases to build an ensemble of classifiers. At each round of the algorithm, each site processes its own data locally, and calculates all needed information. A center site will collect information from all sites and build the globa...

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This paper addresses using an ensemble of classification methods for recognizing totally unconstrained handwritten numerals. Due to a great variety of individual writing styles, the problem is very diffic...

Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contracti...

Image classification is one of the most important tasks of remote sensing information processing used for object recognition. In this paper, a novel scheme is proposed to improve the accuracy of hyperspectral image classification by amalgamating multiple feature vector sets and ensemble methods with different classifiers. Extracting the texture, color and object features of the satellite images...

Broad classes of statistical classification algorithms have been developed and applied successfully to a wide range of real world domains. In general, ensuring that the particular classification algorithm matches the properties of the data is crucial in providing results that meet the needs of the particular application domain. One way in which the impact of this algorithm/application match can...

An electrocardiogram (ECG) is a bioelectrical signal which records the heart’s electrical activity versus time. The interpretation of ECG signal is an application of pattern recognition. The techniques used in this paper comprise: signal preprocessing, R peak detection, QRS reconstruction, RR interval detection, feature extraction and linear classifier model versus ensemble of classifier model....

We propose a novel stream data classification technique to detect Peer to Peer botnet. Botnet traffic can be considered as stream data having two important properties: infinite length and drifting concept. Thus, stream data classification technique is more appealing to botnet detection than simple classification technique. However, no other botnet detection approaches so far have applied stream...