NORMALIZED GEOMETRIC INDEX: A SCALE FOR CLASSIFIER SELECTION

For years, the Machine Learning community has focused on developing efficient algorithms that can produce very accurate classifiers. However, it is often much easier to find several good classifiers based on dataset combination, instead of single classifier applied on deferent datasets. The advantages of using classifier dataset combinations instead of a single one are twofold: it helps lowering the computational complexity by using simpler models, and it can improve the classification accuracy and performance. Most Data mining applications are based on pattern matching algorithms, thus improving the performance of the classification has a positive impact on the quality of the overall data mining task. Since combination strategies proved very useful in improving the performance, these techniques have become very important in applications such as Cancer detection, Speech Technology and Natural Language Processing .The aim of this paper is basically to propose proprietary metric, Normalized Geometric Index (NGI) based on the latent properties of datasets for improving the accuracy of data mining tasks