Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble techniques are known to be very useful in improving the generalization ability of a classifier. The random subspace ensemble technique is a simple but effective method of constructing ensemble classifiers, in which some features are randomly d...

Recent results in solving classification problems indicate that the use of ensembles classifier models often leads to improved performance over using single classifier models [1, 2, 3, 4]. In this talk, we discuss heterogeneous ensemble classifier models, where the member classifier models are not of the same model type. A discussion of the issues associated with creating such classifiers along...

Classical approaches for network traffic classification are based on port analysis and packet inspection. Recent studies indicate that network protocols can be recognised more accurately using the flow statistics of the TCP connection. We propose a classifier selection ensemble for a fast and accurate verification of network protocols. Using the requested port number, the classifier selector di...

A classifier ensemble is a group of individual base classifiers. Each classifier is trained individually by modifying the given data set to achieve diversity. During the testing phase the results given by each classifier are collected to give the final result using a technique called as majority voting. Empirical results prove that diversity amongst the base classifiers improves the accuracy of...

We formulate the problem of creating an optimal classifier ensemble as an optimization problem and apply genetic algorithms to the problem. A pool of 25 individual classifiers is created by training SVM-based classifiers on various features and by varying SVM kernel parameters. A subset of the classifiers selected from the above classifier pool, generated using the proposed optimization techniq...

Background and Purpose: Nowadays, breast cancer is reported as one of the most common cancers amongst women. Early detection of the cancer type is essential to aid in informing subsequent treatments. The newest proposed breast cancer detectors are based on deep learning. Most of these works focus on large-datasets and are not developed for small datasets. Although the large datasets might lead ...

Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with such acquired data, we can predict the class of new sample, whether it is normal or patient. For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to...