Application of the feature-detection rule to the Negative Selection Algorithm

Abstract

The Negative Selection Algorithm developed by Forrest et al. was inspired by the way in which T-cell lymphocytes mature within the thymus before being released into the blood system. The mature T-cell lymphocytes exhibit an interesting characteristic, in that they are only activated by non-self cells that invade the human body. The Negative Selection Algorithm utilises an affinity matching function to ascertain whether the affinity between a newly generated (NSA) T-cell lymphocyte and a self-cell is less than a particular threshold; that is, whether the T-cell lymphocyte is activated by the self-cell. T-cell lymphocytes not activated by self-sells become mature T-cell lymphocytes. A new affinity matching function termed the feature-detection rule is introduced in this paper. The feature-detection rule utilises the interrelationship between both adjacent and non-adjacent features of a particular problem domain to determine whether an antigen is activated by an artificial lymphocyte. The performance of the feature-detection rule is contrasted with traditional affinity matching functions, currently employed within Negative Selection Algorithms, most notably the r-chunks rule (which subsumes the r-contiguous bits rule) and the hamming distance rule. This paper shows that the feature-detection rule greatly improves the detection rates and false alarm rates exhibited by the NSA (utilising the r-chunks and hamming distance rule) in addition to refuting the way in which permutation masks are currently being applied in artificial immune systems.

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

“…Meanwhile, the algorithm will fall into local optimum because of unreasonable initialization, so the richness of population diversity must be taken into account during the chromosomes initialization, and, in order to ensure the diversity of population, the hamming distance, the adequacy coefficient, and the index of maximum and minimum level [48] are proposed to calculate a survival probability for the individuals; while, in this paper, the hamming distance is represented by the fitness value of population to solve the problem of diversity of population, it sets the value into 5% which is the profit rate of total orders, and the Processing Material Figure 3: The scheduling order. Production task coding 1, 2, 3, .…”

Distribution estimation based negative selection algorithm (DENSA)

“…In VSRNSA self-samples have variable-sized radiuses which are based on the total distance of every sample to other self-samples. In [23], a new affinity matching function termed the feature-detection rule was presented to define a more operative matching function for NSA. The affinity measure in [23] benefits from the inter relationship between both adjacent and non-adjacent features of a particular problem domain.…”

Application of Machine Learning Approaches in Intrusion Detection System: A Survey

“…Wisconsin Breast cancer [16] has multivariate data types, all 10 attributes are integer types and it has 699 instances. Glass [32] Although the study briefly highlights public datasets like KDD cup 99, DARPA 1998, DARPA 2000 being considered as standard datasets for intrusion detection system. DARPA dataset contains around 1.5 million traffic instances [36].…”

“…The number of papers using KDD cup 1999 data set yields a peak in 2011 and in total 20 research papers has mentioned KDD Cup 1999 as their dataset. Car evolution dataset [32] contains 1,728 instances with 6 attributes, attribute types are categorical. Wisconsin Breast cancer [16] has multivariate data types, all 10 attributes are integer types and it has 699 instances.…”