Kernel Extended Real-Valued Negative Selection Algorithm (KERNSA)

Artificial Immune Systems (AISs) are a type of statistical Machine Learning (ML) algorithm based on the Biological Immune System (BIS) applied to classification problems. Inspired by increased performance in other ML algorithms when combined with kernel methods, this research explores using kernel methods as the distance measure for a specific AIS algorithm, the Real-valued Negative Selection Algorithm (RNSA). This research also demonstrates that the hard binary decision from the traditional RNSA can be relaxed to a continuous output, while maintaining the ability to map back to the original RNSA decision boundary if necessary. Continuous output is used in this research to generate Receiver Operating Characteristic (ROC) curves and calculate Area Under Curves (AUCs), but can also be used as a basis of classification confidence or probability. The resulting Kernel Extended Real-valued Negative Selection Algorithm (KERNSA) offers performance improvements over a comparable RNSA implementation. Using the Sigmoid kernel in KERNSA seems particularly well suited (in terms of performance) to four out of the eighteen domains tested

The suitability of the dendritic cell algorithm for robotic security applications

The implementation and running of physical security systems is costly and potentially hazardous for those employed to patrol areas of interest. From a technial perspective, the physical security problem can be seen as minimising the probability that intruders and other anomalous events will occur unobserved. A robotic solution is proposed using an artificial immune system, traditionally applied to software security, to identify threats and hazards: the dendritic cell algorithm. It is demonstrated that the migration from the software world to the hardware world is achievable for this algorithm and key properties of the resulting system are explored empirically and theoretically. It is found that the algorithm has a hitherto unknown frequency-dependent component, making it ideal for filtering out sensor noise. Weaknesses of the algorithm are also discovered, by mathematically phrasing the signal processing phase as a collection of linear classifiers. It is concluded that traditional machine learning approaches are likely to outperform the implemented system in its current form. However, it is also observed that the algorithm’s inherent filtering characteristics make modification, rather than rejection, the most beneficial course of action. Hybridising the dendritic cell algorithm with more traditional machine learning techniques, through the introduction of a training phase and using a non-linear classification phase is suggested as a possible future direction

The suitability of the dendritic cell algorithm for robotic security applications

The implementation and running of physical security systems is costly and potentially hazardous for those employed to patrol areas of interest. From a technial perspective, the physical security problem can be seen as minimising the probability that intruders and other anomalous events will occur unobserved. A robotic solution is proposed using an artificial immune system, traditionally applied to software security, to identify threats and hazards: the dendritic cell algorithm. It is demonstrated that the migration from the software world to the hardware world is achievable for this algorithm and key properties of the resulting system are explored empirically and theoretically. It is found that the algorithm has a hitherto unknown frequency-dependent component, making it ideal for filtering out sensor noise. Weaknesses of the algorithm are also discovered, by mathematically phrasing the signal processing phase as a collection of linear classifiers. It is concluded that traditional machine learning approaches are likely to outperform the implemented system in its current form. However, it is also observed that the algorithm’s inherent filtering characteristics make modification, rather than rejection, the most beneficial course of action. Hybridising the dendritic cell algorithm with more traditional machine learning techniques, through the introduction of a training phase and using a non-linear classification phase is suggested as a possible future direction