Building a classification model from thousands of available predictor variables with a relatively small sample size presents challenges for most traditional classification algorithms. When the number of samples is much smaller than the number of predictors, there can be a multiplicity of good classification models. An ensemble classifier combines multiple single classifiers to improve classific...

Ensemble of classifiers increases the performance of the classification since the decision of many experts are fused together to generate the resultant decision for prediction making. Deep learning is a classification algorithm where along with the basic learning technique, fine tuning learning is done for improved precision of learning. Deep classifier ensemble learning is having a good scope ...

Apart from the dimensionality problem, the uncertainty of Microarray data quality is another major challenge of Microarray classification. Microarray data contains various levels of noise and quite often are high levels of noise, and these data lead to unreliable and low accuracy analysis as well as the high dimensionality problem. In this paper, we propose a new Microarray data classification ...

For the opinion analysis task on traditional Chinese texts at NTCIR-7, supervised approaches and ensemble techniques have been used and compared in our participating system. Two kinds of supervised approaches were employed here: 1) the supervised lexicon-based approach, and 2) machine learning approaches. Ensemble techniques were also used to combine the results given by different approaches. B...

Ensemble is a representative technique for improving classification performance by combining a set of classifiers. It is required to maintain the diversity among base classifiers for effective ensemble. Conventional ensemble approaches construct various classifiers by estimating the similarity on the output patterns of them, and combine them with several fusion methods. Since they measure the s...

not been submitted before for any degree or examination in any other University. Abstract This study presents experimental investigations on supervised ensemble classification for land cover classification. Despite the arrays of classifiers available in machine learning to create an ensemble, knowing and understanding the correct classifier to use for a particular dataset remains a major challe...

Classifying patterns into two classes is a typical problem of binary classification in pattern recognition. Binary classification is an industrial problem in many fields like medicine, search mechanism, diagnostic of disease in humans, security and many other aspects. In this paper, we have proposed a random subspace based ensemble data dependent classification model for the binary classificati...

Hierarchical classification problems gained increasing attention within the machine learning community, and several methods for hierarchically structured taxonomies have been recently proposed, with applications ranging from classification of web documents to bioinformatics. In this paper we propose a novel ensemble algorithm for multilabel, multi-path, tree-structured hierarchical classificati...

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...