The performance of m-out-of-n bagging with and without replacement in terms of the sampling ratio (m/n) is analyzed. Standard bagging uses resampling with replacement to generate bootstrap samples of equal size as the original training set mwor = n. Without-replacement methods typically use half samples mwr = n/2. These choices of sampling sizes are arbitrary and need not be optimal in terms of...

To learn any problem, many classifiers have been introduced so far. Each of these classifiers has many strengths (positive aspects) and weaknesses (negative aspects) that make it suitable for some specific problems. But there is no powerful solution to indicate which classifier is the best classifier (or at least a good one) for a special problem. Fortunately the ensemble learning provides us w...

A new version of the a multi-agent system to aid in real estate appraisal, called MAREA-2, was introduced. The system is being developed using Java Spring Framework and is intended for industrial application in cadastral information centres. The major part of the study was devoted to investigate the performance of Bagging, Subagging, and Repeated crossvalidation models. The overall result of ou...

Statistical inferences based on small dimension samples represent a big problem and a made to measure challenge. In the biomedical domain there are many situations when costs or ethical reasons enforce that only a few data are collected. Nevertheless, inferences must be made. In this paper, an alternative to the classical statistical approach is discussed. The article is focused on a simulation...

A new regression technique based on Vapnik’s concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend...

We present a package for R language containing a set of tools for regression using ensembles of learning machines and for time series forecasting. The package contains implementations of Bagging and Adaboost for regression, and algorithms for computing mutual information, autocorrelation and false nearest neighbors.

Ensemble classification methods that independently construct component models (e.g., bagging) improve accuracy over single models by reducing the error due to variance. Some work has been done to extend ensemble techniques for classification in relational domains by taking relational data characteristics or multiple link types into account during model construction. However, since these approac...

Direct feedback of users of search engines by click information is naturally noisy. Ranking models that integrate such feedback in their training process must cope with this noise. In worst case such noise can lead to large variance among the results for different queries in the resulting rankings. We propose to integrate model averaging like bagging and random forest methods to reduce the vari...

In the paper the investigation of m-out-of-n bagging with and without replacement using genetic neural networks is presented. The study was conducted with a newly developed system in Matlab to generate and test hybrid and multiple models of computational intelligence using different resampling methods. All experiments were conducted with real-world data derived from a cadastral system and regis...

This paper proposes an approach to improve statistical word alignment with ensemble methods. Two ensemble methods are investigated: bagging and cross-validation committees. On these two methods, both weighted voting and unweighted voting are compared under the word alignment task. In addition, we analyze the effect of different sizes of training sets on the bagging method. Experimental results ...