Paper Title
Artificial Activation System the Enzymatic Model for Classification of Imbalanced Data

Imbalanced Dataset is a very common problem in classification of data. In supervised learning many techniques have been developed to tackle the problem of imbalanced training sets. Such techniques have been divided into two groups: at algorithm level and at the data level. Data level groups emphasized are those that try to balance the training sets by reducing the larger class through elimination of samples or increasing the smaller ones by constructing new samples known as Under sampling and Over sampling respectively. This paper proposes a new hybrid method for the classification of imbalanced datasets through construction of new samples using the Synthetic Minority Over sampling technique together with the application of a new technique Enzyme-computation called Artificial Activation System. The proposed method Enzyme-computation has been comparatively studied, validated and supported by an experimental study and shows good results. Keywords- Imbalanced Datasets, Oversampling, Under Sampling, rough set theory, Enzyme-computation model