Numerical link-based similarity techniques have proven effective for identifying similar objects in the Internet and publication domains. However, for cases involving unduly high similarity measures, these methods usually ...

The accuracy of methods for the assessment of mammographic risk analysis is heavily related to breast tissue characteristics. Previous work has demonstrated considerable success in developing an automatic breast tissue ...

Classifier ensembles constitute one of the main research directions in machine learning and data mining. Ensembles allow higher accuracy to be achieved which is otherwise often not achievable with a single classifier. A ...

Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. ...

Rough set theory provides a useful mathematical foundation for developing automated computational systems that can help understand and make use of imperfect knowledge. Since its introduction, this theory has been successfully ...

Nearest neighbour (NN) approaches are inspired by the way humans make decisions, comparing a test object to previously encountered samples. In this paper, we propose an NN algorithm that uses the lower and upper approximations ...

In cancer chemotherapy, multidrug resistance (MDR) is a major clinical problem which occurs by an influential mechanism and which leads to the failure of cancer chemotherapy and/or a relapse of the cancer. In this study, ...

A new approach to fuzzy rule induction from historical data is presented. The implemented system - FRANTIC - is a tested on a simple classification problem against a fuzzy tree induction algorithm, a genetic algorithm, and ...

Many real-world problems require the development and application of algorithms that automatically generate human interpretable knowledge from historical data. Most existing algorithms for rule induction from imprecise data ...

In the past decade, compositional modelling (CM) has established itself as the predominant knowledge-based approach to construct mathematical (simulation) models automatically. Although it is mainly applied to physical ...