Clonal selection based Artificial Immune System for generalized pattern recognition

Abstract

The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

A new clonal selection algorithm based on radius regularization of anomaly detectors

“…By reviewing the researches in clonal selection field, we can taxonomy them in 3 categories; (i) the researches that consider the main aspects of operation algorithm: Cloning, mutation and selection [15,16], (ii) the researches that Regulate parameters to achieve high accuracy [1,14,2] and (iii) the researches that use clonal selection to optimize the proposed algorithms or their parameters [3,4,8,9,10].…”

“…The main purpose of HIS is to recognize all cells (or molecules) within the body and categorize them as self or non-self [4]. HIS consists of a collection of lymphocyte cells (T-cells and B-cells) that produce secretion of antibodies as a response to antigens such as bacteria, viruses and tumor cells [3].…”