Data gravitation based classification

Experimental results illustrate that the proposed DGC method is very efficient for data classification and feature selection and a novel feature selection algorithm is investigated based on the idea of DGC and weighted features.

Abstract

Data gravitation based classification (DGC) is a novel data classification technique based on the concept of data gravitation. The basic principle of DGC algorithm is to classify data samples by comparing the data gravitation between the different data classes. In the DGC model, a kind of "force" called data gravitation between two data samples is computed. Data from the same class are combined as a result of gravitation. On the other hand, data gravitation between different data classes can be compared. A larger gravitation from a class means the data sample belongs to a particular class. One outstanding advantage of the DGC, in comparison with other classification algorithms is its simple classification principle with high performance. This makes the DGC algorithm much easier to be implemented. Feature selection plays an important role in classification problems and a novel feature selection algorithm is investigated based on the idea of DGC and weighted features. The proposed method is validated by using 12 well-known classification data sets from UCI machine learning repository. Experimental results illustrate that the proposed method is very efficient for data classification and feature selection.