i need matlab code of artificial bee colony for image feature selection

matlab code of artificial bee colony for feature selection

Abstract

This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.

Introduction
Pattern recognition and data mining are the techniques that allow for the acquirement of meaningful information from large-scale data using a computer program. Nowadays, these techniques are extensively used, particularly in the military, medical, and industrial application fields, since there is a continuously increasing amount and type of data in these areas, due to advanced data acquisition systems. For this reason, for the obtained data set, data reduction algorithms are needed for filtering, priority sorting, and providing redundant measurements to detect the feature selection. By using these algorithms, quality data is obtained, which in turn raises the quality of the analyzing systems or the success of the recognition systems. In particular, medical applications with ever-increasing popularity and use of advanced technology are the most important field in which these algorithms are used. Many new algorithms developed in the field of medicine are tested on the disease data presented for the common use of all the scientists, and their performances are compared. The datasets from UCI database are very popular for this purpose. The algorithm developed and tested on hepatitis, liver disorders, and diabetes data from UCI was compared with studies in the literature that use the same datasets. These data sets consist of diseases that are commonly encountered in society and significantly reduce the quality of life of patients. The selected data sets are comprised of a variety of test and analysis device data and personal information about the patients. The main objective our work is the integration of the developed systems to these test and analysis devices and to provide a fully automatic assistance to the physician in the creation of diagnosis systems for the diseases. The diagnosis systems, which can be easily used during routine controls, will make the timely information and the early treatment of patients possible.

For the dataset recognition aiming diagnosis of the diseases, we propose a two-stage approach. The first stage has used the clustering with ABC algorithm as selection criteria for feature selection, and, thus, more effective feature selection methods have been constituted. Hence, it has been made possible both to select the related features faster and to reduce the feature vector dimensions. In the second stage, the reduced data was given to the SVM classifier and the accuracy rates were determined. The k-fold cross-validation method was used for improving the classifier reliability. The datasets we have worked on have been described in the Background section. As it is seen from the results, the performance of the proposed method is highly successful compared to other results attained and seems very promising for pattern recognition applications.