An unsupervised vegetation classification algorithm based immune | IEEE Conference Publication | IEEE Xplore

An unsupervised vegetation classification algorithm based immune


Abstract:

A novel artificial immune-based algorithm in predicting forest cover types with cartographic variables, referred to as POOTAI, is presented. Firstly, the definition of im...Show More

Abstract:

A novel artificial immune-based algorithm in predicting forest cover types with cartographic variables, referred to as POOTAI, is presented. Firstly, the definition of immune cell, antibody, and antigen are given. Then, the dynamic models of immune response, immune regulation and immune memory are evolved, and the corresponding equations are established. Finally, it is tested by the well-known forest cover types data set of UCI (University of California at Irvine) and compared with other known algorithms. POOTAI shows that the classification accuracy is increased to 90.17%, which is higher than other classification algorithms. It has some good features such as continuous learning, dynamic adjustment, characteristics memory, and etc.
Date of Conference: 10-12 August 2010
Date Added to IEEE Xplore: 23 September 2010
ISBN Information:

ISSN Information:

Conference Location: Yantai, China

References

References is not available for this document.