The problem of improving rough set based expert systems by modifying a notion of reduct is discussed. The notion of approximate reduct is introduced, as well as some proposals of quality measure for such a reduct. The complete classifying system based on approximate reducts is presented and discussed. It is proved that the problem of finding optimal set of classifying agents based on approximat...

We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selection as a multi-label classification (MLC) problem, which takes as input time-series data (students’ learning data) and outputs a summary of these data (feedback). Unlike previous work, this method considers all data simul...

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...

This paper presents a new topic of automatic recognition of bank note serial numbers, which will not only facilitate the prevention of forgery crimes, but also have a positive impact on the economy. Among all the different currencies, we focus on the study of RMB (renminbi bank note, the paper currency used in China) serial numbers. For evaluation, a new database NUST-RMB2013 has been collected...

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...