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11th International Workshop, IWLCS 2008, Atlanta, GA, USA, July 13, 2008, and 12th International Workshop, IWLCS 2009, Montreal, QC, Canada, July 9, 2009, Revised Selected Papers
The Trading Agent Competition in its category Supply Chain Management (TAC SCM) is an international forum where teams develop agents that control a computer assembly company in a simulated environment. TAC SCM involves the following problems: to determine when to send offers, decide the final sales prices of the goods offered and plan the factory and delivery schedules. In this work, we developed...
This paper extends current LCS research into financial time series forecasting by analysing the performance of agents utilising mathematical technical indicators for both environment classification and in selecting actions to be executed. It compares these agents with traditional models which only use such indicators to classify the environment and exit at the close of the next day. It is proposed...
In experimental sciences, diversity tends to difficult predictive models’ proper generalization across data provided by different laboratories. Thus, training on a data set produced by one lab and testing on data provided by another lab usually results in low classification accuracy. Despite the fact that the same protocols were followed, variability on measurements can introduce unforeseen variations...
Despite many successful applications of the XCS classifier system, a rather crucial aspect of XCS’ learning mechanism has hardly ever been modified: exactly two classifiers are reproduced when XCSF’s iterative evolutionary algorithm is applied in a sampled problem niche. In this paper, we investigate the effect of modifying the number of reproduced classifiers. In the investigated problems, increasing...
Function approximation is an important technique used in many different domains, including numerical mathematics, engineering, and neuroscience. The XCSF classifier system is able to approximate complex multi-dimensional function surfaces using a patchwork of simpler functions. Typically, locally linear functions are used due to the tradeoff between expressiveness and interpretability. This work discusses...
We investigate the use of NVIDIA’s Compute Unified Device Architecture (CUDA) to speed up matching in classifier systems. We compare CUDA-based matching and CPU-based matching on (i) real inputs using interval-based conditions and on (ii) binary inputs using ternary conditions. Our results show that on small problems, due to the memory transfer overhead introduced by CUDA, matching is faster when...
This paper presents CSar, a Michigan-style learning classifier system designed to extract quantitative association rules from streams of unlabeled examples. The main novelty of CSar with respect to the existing association rule miners is that it evolves the knowledge online and it is thus prepared to adapt its knowledge to changes in the variable associations hidden in the stream of unlabeled data...
Proposed is an automatic system for creating pattern generators and recognizers that may provide new and human-independent insight into the pattern recognition problem. The system is based on a three-cornered coevolution of image-transformation programs.
XCS with computed prediction, namely XCSF, has been recently extended in several ways. In particular, a novel prediction update algorithm based on recursive least squares and the extension to polynomial prediction led to significant improvements of XCSF. However, these extensions have been studied so far only on single step problems and it is currently not clear if these findings might be extended...
XCSF is a modern form of Learning Classifier System (LCS) that has proven successful in a number of problem domains. In this paper we exploit the modular nature of XCSF to include a number of extensions, namely a neural classifier representation, self-adaptive mutation rates and neural constructivism. It is shown that, via constructivism, appropriate internal rule complexity emerges during learning...
This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments containing aliasing squares. This type of environment is often used in reinforcement learning literature to assess the performances of learning methods when facing problems containing non markovian situations. Through this...
Biomedical datasets pose a unique challenge for machine learning and data mining techniques to extract accurate, comprehensible and hidden knowledge from them. In this paper, we investigate the role of a biomedical dataset on the classification accuracy of an algorithm. To this end, we quantify the complexity of a biomedical dataset in terms of its missing values, imbalance ratio, noise and information...
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