Discriminating self from non-self with finite mixtures of multivariate Bernoulli distributions

Affinity functions are the core components in negative selection to discriminate self from non-self. It has been shown that affinity functions such as the r-contiguous distance and the Hamming distance are limited applicable for discrimination problems such as anomaly detection. We propose to model self as a discrete probability distribution specified by finite mixtures of multivariate Bernoulli distributions. As by-product one also obtains information of non-self and hence is able to discriminate with probabilities self from nonself. We underpin our proposal with a comparative study between the two affinity functions and the probabilistic discrimination. Categories and Subject Descriptors G.3 [Probability and Statistics]: [Multivariate Statistics, Experimental Design]; I.6.4 [Simulation and Modeling]: Model Validation and Analysis General Terms Algorithms

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Thomas Stibor
• Keywords: Affinity Functions, Discrete Probability Distribution, GECCO 2008, Multivariate Bernoulli Distributions, Optimization