-Classification is one of the data mining techniques that analyses a given data set and induces a model for each class based on their features present in the data. Bagging and boosting are heuristic approaches to develop classification models. These techniques generate a diverse ensemble of classifiers by manipulating the training data given to a base learning algorithm. They are very successfu...

Many researchers have shown that ensemble methods such as Boosting and Bagging improve the accuracy of classification. Boosting and Bagging perform well with unstable learning algorithms such as neural networks or decision trees. Pruning decision tree classifiers is intended to make trees simpler and more comprehensible and avoid over-fitting. However it is known that pruning individual classif...

Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. However, the minority class instances are representing the concept with greater in...

The class imbalance problems have been reported to severely hinder classification performance of many standard learning algorithms, and have attracted a great deal of attention from researchers of different fields. Therefore, a number of methods, such as sampling methods, cost-sensitive learning methods, and bagging and boosting based ensemble methods, have been proposed to solve these problems...

In this paper, we derive a novel probabilistic model of boosting as a Product of Experts. We re-derive the boosting algorithm as a greedy incremental model selection procedure which ensures that addition of new experts to the ensemble does not decrease the likelihood of the data. These learning rules lead to a generic boosting algorithm POEBoost which turns out to be similar to the AdaBoost alg...

An ensemble of classifiers consists of a set of individually trained classifiers whose predictions are combined when classifying new instances. The resulting ensemble is generally more accurate than the individual classifiers it consists of. In particular, one of the most popular ensemble methods, the Boosting approach, improves the predictive performance of weak classifiers, which can achieve ...

Ensemble learning algorithms such as boosting can achieve better performance by averaging over the predictions of base hypotheses. However, most existing algorithms are limited to combining only a finite number of hypotheses, and the generated ensemble is usually sparse. It has recently been shown that the support vector machine (SVM) with a carefully crafted kernel can be used to construct a n...

Learning good classifier models of rare events is a challenging task. On such problems, the recently proposed two-phase rule induction algorithm, PNrule, outperforms other non-meta methods of rule induction. Boosting is a strong meta-classifier approach, and has been shown to be adaptable to skewed class distributions. PNrule’s key feature is to identify the relevant false positives and to coll...

Microarray data analysis and classification has demonstrated convincingly that it provides an effective methodology for the effective diagnosis of diseases and cancers. Although much research has been performed on applying machine learning techniques for microarray data classification during the past years, it has been shown that conventional machine learning techniques have intrinsic drawbacks...