A Multi-Layered Immune Inspired Approach to Data Mining

Soft Computing has been described as computational systems that exploit tolerance for imprecision, uncertainty, partial truth and approximation [8]. Such systems include artificial neural networks, fuzzy systems, evolutionary algorithms and probabilistic reasoning. Artificial Immune Systems (AIS) have recently been proposed as an additional soft computing paradigm [5]. It has been argued that AIS exhibit similar characteristics to other soft computing paradigms and therefore can be used to complement and augment existing soft computing techniques. AIS can be defined as adaptive systems, inspired by theoretical immunology and observed immune functions, principles and models which are applied to problem solving [4]. This paper presents a new immune inspired algorithm that augments the AIS framework proposed in [4, 5]. Preliminary results on three numerical data sets are given and future directions are discussed. This paper argues that this new algorithm adheres to the soft computing philosophy and therefore proposes this as a novel algorithm with regard to both soft computing and immune inspired algorithms.