Rote-LCS learning classifier system for classification and prediction

Machine Learning (ML) involves the use of computer algorithms to solve for approximate solutions to problems with large, complex search spaces. Such problems have no known solution method, and search spaces too large to allow brute force search to be feasible. Evolutionary algorithms (EA) are a subset of machine learning algorithms which simulate fundamental concepts of evolution. EAs do not guarantee a perfect solution, but rather facilitate convergence to a solution of which the accuracy depends on a given EA\u27s learning architecture and the dynamics of the problem. Learning classifier systems (LCS) are algorithms comprising a subset of EAs. The Rote-LCS is a novel Pittsburgh-style LCS for supervised learning problems. The Rote models a solution space as a hyper-rectangle, where each independent variable represents a dimension. Rote rules are formed by binary trees with logical operators (decision trees) with relational hypotheses comprising the terminal nodes. In this representation, sub-rules (minor-hypotheses) are partitions on hyper-planes, and rules (major-hypotheses) are multidimensional partitions. The Rote-LCS has exhibited very high accuracy on classification problems, particularly Boolean problems, thus far. The Rote-LCS offers an additional attribute uncommon among machine learning algorithms - human readable solutions. Despite representing a multidimensional search space, Rote solutions may be graphed as two-dimensional trees. This makes the Rote-LCS a good candidate for supervised classification problems where insight is needed into the dynamics of a problem. Solutions generated by Rote-LCS could prospectively be used by scientists to form hypotheses regarding interactions between independent variables of a given problem. --Abstract, page iv

Learning classifier systems from first principles: A probabilistic reformulation of learning classifier systems from the perspective of machine learning

Learning Classifier Systems (LCS) are a family of rule-based machine learning methods. They aim at the autonomous production of potentially human readable results that are the most compact generalised representation whilst also maintaining high predictive accuracy, with a wide range of application areas, such as autonomous robotics, economics, and multi-agent systems. Their design is mainly approached heuristically and, even though their performance is competitive in regression and classification tasks, they do not meet their expected performance in sequential decision tasks despite being initially designed for such tasks. It is out contention that improvement is hindered by a lack of theoretical understanding of their underlying mechanisms and dynamics.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Support vector regression for classifier prediction

In this paper we introduce XCSF with support vector prediction: the problem of learning the prediction function is solved as a support vector regression problem and each classifier exploits a Support Vector Machine to compute the prediction. In XCSF with support vector prediction, XCSFsvm, the genetic algorithm adapts classifier conditions, classifier actions, and the SVM kernel parameters. We compare XCSF with support vector prediction to XCSF with linear prediction on the approximation of four test functions. Our results suggest that XCSF with support vector prediction compared to XCSF with linear prediction (i) is able to evolve accurate approximations of more difficult functions, (ii) has better generalization capabilities and (iii) learns faster