An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

Mengling Zhao,Hongwei Liu

doi:10.1155/2015/839081

Abstract

As a computational intelligence method, artificial immune network (AIN) algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN). The new method uses the association rules to represent immune cells and mine the best association rules rather than searching optimal clustering centers. The proposed algorithm has been extensively compared with artificial immune network classification (AINC) algorithm, artificial immune network classification algorithm based on self-adaptive PSO (SPSO-AINC), and PSO-AINC over several large-scale data sets, target recognition of remote sensing image, and segmentation of three different SAR images. The result of experiment indicates the superiority of ARM-AIN in classification accuracy and running time.

Highlights

In data mining field, classification is one of the most important issues which have a wide range of applications
The association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN)
We introduced the associate rules into the artificial immune network and improved the associate rules mining algorithm based on artificial immune network (AIN-ARM) for large-scale data, remote sensing image, and synthetic aperture radar (SAR) image classification

Summary

Introduction

Classification is one of the most important issues which have a wide range of applications. For its good control over the population size, Watkins and Timmis made a series of improvements of this algorithm [8,9,10], and de Castro and von Zuben proposed Self-Stabilizing Artificial Immune System (SSAIS) [11] on the basis of RLAIS for continuous analysis of time-varying data. Existing artificial immune network classification algorithms are used to find the affinity of immune cells through the calculation of the distance, which determines the classification result far from ideal when the characteristics of the data to be classified are nominal characteristic value. We introduced the associate rules into the artificial immune network and improved the associate rules mining algorithm based on artificial immune network (AIN-ARM) for large-scale data, remote sensing image, and SAR image classification

Background

The Proposed Algorithm

Key Technologies of Algorithm

Experimental Results and Analysis

Conclusion