The Negative Selection algorithm is inspired by the self-nonself discrimination behavior observed in the mammalian acquired immune system. The clonal selection theory of acquired immunity accounts for the adaptive behavior of the immune system including the ongoing selection and proliferation of cells that select-for potentially harmful (and typically foreign) material in the body. An interesting aspect of this process is that it is responsible for managing a population of immune cells that do not select-for the tissues of the body, specifically it does not create self-reactive immune cells known as auto-immunity. This problem is known as self-nonself discrimination and it involves the preparation and on going maintenance of a repertoire of immune cells such that none are auto-immune. This is achieved by a negative selection process that selects-for and removes those cells that are self-reactive during cell creation and cell proliferation. This process has been observed in the preparation of T-lymphocytes, naive versions of which are matured using both a positive and negative selection process in the thymus.
The self-nonself discrimination principle suggests that the anticipatory guesses made in clonal selection are filtered by regions of infeasibility (protein conformations that bind to self-tissues). Further, the self-nonself immunological paradigm proposes the modeling of the unknown domain (encountered pathogen) by modeling the complement of what is known. This is unintuitive as the natural inclination is to categorize unknown information by what is different from that which is known, rather than guessing at the unknown information and filtering those guesses by what is known.
The information processing principles of the self-nonself discrimination process via negative selection are that of a anomaly and change detection systems that model the anticipation of variation from what is known. The principle is achieved by building a model of changes, anomalies, or unknown (non-normal or non-self) data by generating patterns that do not match an existing corpus of available (self or normal) patterns. The prepared non-normal model is then used to either monitor the existing normal data or streams of new data by seeking matches to the non-normal patterns.
The following algorithm provides a pseudocode listing of the detector generation procedure for the Negative Selection Algorithm.
The following algorithm provides a pseudocode listing of the detector application procedure for the Negative Selection Algorithm.
The Negative Selection Algorithm was designed for change detection, novelty detection, intrusion detection and similar pattern recognition and two-class classication problem domains. Traditional negative selection algorithms used binary representations and binary matching rules such as Hamming distance, and r-contiguous bits.
A data representation should be selected that is most suitable for a given problem domain, and a matching rule is in turn selected or tailored to the data representation. Detectors can be prepared with no prior knowledge of the problem domain other than the known (normal or self) dataset.
The algorithm can be configured to balance between detector convergence (quality of the matches) and the space complexity (number of detectors). The lack of dependence between detectors means that detector preparation and application is inherently parallel and suited for a distributed and parallel implementation, respectively.