| Grant number: | 11/16411-4 |
| Support Opportunities: | Regular Research Grants |
| Start date: | December 01, 2011 |
| End date: | November 30, 2013 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal Investigator: | Moacir Antonelli Ponti |
| Grantee: | Moacir Antonelli Ponti |
| Host Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
Abstract
Classification methods often fail to address two common problems in real applications: large data sets and imbalanced class data. Data sets with large number of elements are becoming more pervasive due to the availability of sensors and storage technology, and by the nature of some applications such as financial transactions, network logs and bioinformatics. Multiple classifier systems can be used both to paralelize or distribute the processing, and to allow the undersampling of the training set, so that large data sets are feasible to be used in classification tasks. Methods derived from Bagging techniques can be used to generate diversity in multiple classifiers, trained in parallel with small amount of data, resulting in similar or higher accuracies when compared to a simple classification. The use of ensembles of classifiers also have the potential to minimize the class-imbalance problem, by sampling methods applied with Boosting techniques, aided by concept drift studies. This projects aims the investigation of these two issues by the point of view of multiple classifier systems, researching solutions with applications in various areas. (AU)
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