On XCSR for electronic fraud detection

Behdad, Mohammad; Barone, Luigi; French, Tim; Bennamoun, Mohammed

doi:10.1007/s12065-012-0076-5

Cited by 13 publications

(6 citation statements)

References 25 publications

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“…Although our algorithm has different goal to the KDD (Knowledge Discovery and Datamining) competition, we are still interested in a comparison to single-type attacks. The winner of KDD cup and a recent research had an overall accuracy of 92.7% and 96.5%, respectively [25] compared with the rate of 95.9% in this study. For the massive unknown variants from intrusion hackers, a small difference in the accuracy number may not be meaningful in performance comparison.…”

Section: Resultssupporting

confidence: 43%

Detecting Mixed-Type Intrusion in High Adaptability Using Artificial Immune System and Parallelized Automata

Chou

Chen³

et al. 2020

Applied Sciences

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This study applies artificial immune system and parallelized finite-state machines to construct an intrusion detection algorithm for spotting hidden threats in massive number of packets. Existing intrusion detections are mostly not focused on adaptability for mixed and changing attacks, which results in low detection rate in new and mixed-type attacks. Using the characteristics of artificial immune and state transition can address the attacks in evolutionary patterns and track the anomalies in nonconsecutive packets. The proposed immune algorithm in this study is highly efficient based on a selection step in multi-island migration. Result shows that the algorithm can effectively detect mixed-type attacks and obtains an overall accuracy of 95.9% in testing data.

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Section: Resultssupporting

confidence: 43%

Detecting Mixed-Type Intrusion in High Adaptability Using Artificial Immune System and Parallelized Automata

Chou

Chen³

et al. 2020

Applied Sciences

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“…Commonly, the action in a classifier rule is represented by a numeric constant. LCS can be applied to a wide range of problems including classification, data mining, function approximation, control, modeling and optimization problems [10,17,22,101]. LCSs have been shown to be robust to small amounts of noise and are often more robust than most machine learning techniques with increasing amounts of noise [22].…”

Section: Motivationmentioning

confidence: 99%

“…Traditionally, generalization in an LCS is achieved by using a special 'don't care' symbol (#) in classifier conditions, which matches any value of a specified attribute in the vector describing the environmental state. LCS can be applied to a wide range of problems including data mining, control, modeling and optimization problems [10,17,101]. LCS have been shown to be robust to small amounts of noise and are often more robust than most machine learning techniques with increasing amounts of noise [22].…”

Section: Learning Classifier Systemsmentioning

confidence: 99%

“…Evolutionary machine learning techniques are powerful tools, which autonomously solve various problems such as classification, optimization, data mining, and function approximation [10,17,101]. However, the vast majority of these techniques are based on supervised learning, e.g.…”

Section: Chapter Summarymentioning

confidence: 99%

“…For example, the function xy + xy can be represented in Sigma notation as Σ(1, 3). The DV1 problem is denoted by the following Sigma notation: Σ (1,2,3,8,9,10,11,13,14,24,25,26,27,28,30,40,41,42,43,46,47,56,57,58,59,61,65,66,67,69,70,71,72,73,74,75,77,78,79,81,82,83,85,86,88,89,90,91,93,94,…”

Section: The Design Verification Problem Domainmentioning

confidence: 99%

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Improving the Scalability of XCS-Based Learning Classifier Systems

Iqbal¹

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<p>Using evolutionary intelligence and machine learning techniques, a broad range of intelligent machines have been designed to perform different tasks. An intelligent machine learns by perceiving its environmental status and taking an action that maximizes its chances of success. Human beings have the ability to apply knowledge learned from a smaller problem to more complex, large-scale problems of the same or a related domain, but currently the vast majority of evolutionary machine learning techniques lack this ability. This lack of ability to apply the already learned knowledge of a domain results in consuming more than the necessary resources and time to solve complex, large-scale problems of the domain. As the problem increases in size, it becomes difficult and even sometimes impractical (if not impossible) to solve due to the needed resources and time. Therefore, in order to scale in a problem domain, a systemis needed that has the ability to reuse the learned knowledge of the domain and/or encapsulate the underlying patterns in the domain. To extract and reuse building blocks of knowledge or to encapsulate the underlying patterns in a problem domain, a rich encoding is needed, but the search space could then expand undesirably and cause bloat, e.g. as in some forms of genetic programming (GP). Learning classifier systems (LCSs) are a well-structured evolutionary computation based learning technique that have pressures to implicitly avoid bloat, such as fitness sharing through niche based reproduction. The proposed thesis is that an LCS can scale to complex problems in a domain by reusing the learnt knowledge from simpler problems of the domain and/or encapsulating the underlying patterns in the domain. Wilson’s XCS is used to implement and test the proposed systems, which is a well-tested, online learning and accuracy based LCS model. To extract the reusable building blocks of knowledge, GP-tree like, code-fragments are introduced, which are more than simply another representation (e.g. ternary or real-valued alphabets). This thesis is extended to capture the underlying patterns in a problemusing a cyclic representation. Hard problems are experimented to test the newly developed scalable systems and compare them with benchmark techniques. Specifically, this work develops four systems to improve the scalability of XCS-based classifier systems. (1) Building blocks of knowledge are extracted fromsmaller problems of a Boolean domain and reused in learning more complex, large-scale problems in the domain, for the first time. By utilizing the learnt knowledge from small-scale problems, the developed XCSCFC (i.e. XCS with Code-Fragment Conditions) system readily solves problems of a scale that existing LCS and GP approaches cannot, e.g. the 135-bitMUX problem. (2) The introduction of the code fragments in classifier actions in XCSCFA (i.e. XCS with Code-Fragment Actions) enables the rich representation of GP, which when couples with the divide and conquer approach of LCS, to successfully solve various complex, overlapping and niche imbalance Boolean problems that are difficult to solve using numeric action based XCS. (3) The underlying patterns in a problem domain are encapsulated in classifier rules encoded by a cyclic representation. The developed XCSSMA system produces general solutions of any scale n for a number of important Boolean problems, for the first time in the field of LCS, e.g. parity problems. (4) Optimal solutions for various real-valued problems are evolved by extending the existing real-valued XCSR system with code-fragment actions to XCSRCFA. Exploiting the combined power of GP and LCS techniques, XCSRCFA successfully learns various continuous action and function approximation problems that are difficult to learn using the base techniques. This research work has shown that LCSs can scale to complex, largescale problems through reusing learnt knowledge. The messy nature, disassociation of message to condition order, masking, feature construction, and reuse of extracted knowledge add additional abilities to the XCS family of LCSs. The ability to use rich encoding in antecedent GP-like codefragments or consequent cyclic representation leads to the evolution of accurate, maximally general and compact solutions in learning various complex Boolean as well as real-valued problems. Effectively exploiting the combined power of GP and LCS techniques, various continuous action and function approximation problems are solved in a simple and straight forward manner. The analysis of the evolved rules reveals, for the first time in XCS, that no matter how specific or general the initial classifiers are, all the optimal classifiers are converged through the mechanism ‘be specific then generalize’ near the final stages of evolution. Also that standard XCS does not use all available information or all available genetic operators to evolve optimal rules, whereas the developed code-fragment action based systems effectively use figure and ground information during the training process. Thiswork has created a platformto explore the reuse of learnt functionality, not just terminal knowledge as present, which is needed to replicate human capabilities.</p>

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