-The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is wellknown that ensemble methods can be used for improving prediction performance. Researchers from various disciplines such as statistics and AI considered the use of ensemble methodology. Meta-learning is a technique that seeks to compute higher-level classifiers (or classification models), c...

We discuss approaches to incrementally construct an ensemble. The first constructs an ensemble of classifiers choosing a subset from a larger set, and the second constructs an ensemble of discriminants, where a classifier is used for some classes only. We investigate criteria including accuracy, significant improvement, diversity, correlation, and the role of search direction. For discriminant ...

Abstract Ensemble classifiers have been investigated by many in the artificial intelligence and machine learning community. Majority voting weighted majority are two commonly used combination schemes ensemble learning. However, understanding of them is incomplete at best, with some properties even misunderstood. In this paper, we present a group these formally under geometric framework. Two key...

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...

The k-nearest-neighbor rule is a well known pattern recognition technique with very good results in a great variety of real classification tasks. Based on the neighborhood concept, several classification rules have been proposed to reduce the error rate of the k-nearest-neighbor rule (or its time requirements). In this work, two new geometrical neighborhoods are defined and the classification r...

Ensembles of classifiers are often used to achieve accuracy greater than any single classifier. The predictions of these classifiers are typically combined together by uniform or weighted voting. In this paper, we approach the ensembles construction under a multi-agent framework. Each individual agent is capable of learning from data, and the agents can either be homogenous (same learning algor...

Previous studies about ensembles of classifiers for bankruptcy prediction and credit scoring have been presented. In these studies, different ensemble schemes for complex classifiers were applied, and the best results were obtained using the Random Subspace method. The Bagging scheme was one of the ensemble methods used in the comparison. However, it was not correctly used. It is very important...

In this paper we describe the ENSEMBLE-ROLLER planner submitted to the Learning Track of the International Planning Competition (IPC). The planner uses ensembles of relational classifiers to generate robust planning policies. As in other applications of machine learning, the idea of the ensembles of classifiers consists of providing accuracy for particular scenarios and diversity to cover a wid...

In a scenario of supervised classification of data, labeled training data is essential. Unfortunately, the process by which those labels are obtained is not error-free, for example due to human nature. The aim of this work is to find out what impact noise on the labels has, and we do so by artificially adding it. An algorithm for the noising procedure is described. Not only individual classifie...