Missing values are an unavoidable issue of many real-world datasets. Dealing with missing values is an essential requirement in classification problem, because inadequate treatment with missing values often leads to large classification errors. Some classifiers can directly work with incomplete data, but they often result in big classification errors and generate complex models. Feature selecti...

Random forests are one of the best performing methods for constructing ensembles. They derive their strength from two aspects: using random subsamples of the training data (as in bagging) and randomizing the algorithm for learning base-level classifiers (decision trees). The base-level algorithm randomly selects a subset of the features at each step of tree construction and chooses the best amo...

A algorithm for computing the PDF of order statistics drawn from discrete parent populations is presented, along with an implementation of the algorithm in a computer algebra system. Several examples and applications, including exact bootstrapping analysis, illustrate the utility of this algorithm. Bootstrapping procedures require that B bootstrap samples be generated in order to perform statis...

If we look a few years back, we will find that ensemble classification model has outbreak many research and publication in the data mining community discussing how to combine models or model prediction with reduction in the error that results. When we ensemble the prediction of more than one classifier, more accurate and robust models are generated. We have convention that bagging, boosting wit...

This work develops formal statistical inference procedures for predictions generated by supervised learning ensembles. Ensemble methods based on bootstrapping, such as bagging and random forests, have improved the predictive accuracy of individual trees, but fail to provide a framework in which distributional results can be easily determined. Instead of aggregating full bootstrap samples, we co...

Ensembles of decision trees often exhibit greater predictive accuracy than single trees alone. Bagging and boosting are two standard ways of generating and combining multiple trees. Boosting has been empirically determined to be the more eeective of the two, and it has recently been proposed that this may be because it produces more diverse trees than bagging. This paper reports empirical nding...

For various biomedical applications, an automated quality assessment is an essential but also complex task. Ensembles of decision trees (EDTs) have proven to be a suitable choice for such classification tasks. Within this contribution we invoke EDTs to assess the usability of ECGs. Our classification relies on the usage of simple spectral features which were derived directly from individual ECG...

The inactive student rate is becoming a major problem in most open universities worldwide. In Indonesia, roughly 36% of students were found to be inactive, in 2005. Data mining had been successfully employed to solve problems in many domains, such as for educational purposes. We are proposing a method for preventing inactive students by mining knowledge from student record systems with several ...

Areal bone mineral density (aBMD) is used in clinical practice to diagnose osteoporosis. In previous studies, aBMD was estimated from diagnostic computed tomography (dCT) images, but a battery of medical tests was also taken that can be used to improve the regression performance. However, it is difficult to exploit the multimodal data as the additional features have poor informativeness and may...

We experimentally evaluate bagging and seven other randomization-based approaches to creating an ensemble of decision-tree classifiers. Unlike methods related to boosting, all of the eight approaches create each classifier in an ensemble independently of the other classifiers in the ensemble. Bagging uses randomization to create multiple training sets. Other approaches, such as those of Dietter...