Identification of the neighborhood and CA rules from spatio-temporal CA patterns

Extracting the rules from spatio-temporal patterns generated by the evolution of cellular automata (CA) usually produces a CA rule table without providing a clear understanding of the structure of the neighborhood or the CA rule. In this paper, a new identification method based on using a modified orthogonal least squares or CA-OLS algorithm to detect the neighborhood structure and the underlying polynomial form of the CA rules is proposed. The Quine-McCluskey method is then applied to extract minimum Boolean expressions from the polynomials. Spatio-temporal patterns produced by the evolution of 1D, 2D, and higher dimensional binary CAs are used to illustrate the new algorithm, and simulation results show that the CA-OLS algorithm can quickly select both the correct neighborhood structure and the corresponding rule

The temporal pattern of trading rule returns and central bank intervention: intervention does not generate technical trading rule profits

This paper characterizes the temporal pattern of trading rule returns and official intervention for Australian, German, Swiss and U.S. data to investigate whether intervention generates technical trading rule profits. High frequency data show that abnormally high trading rule returns precede German, Swiss and U.S. intervention, disproving the hypothesis that intervention generates inefficiencies from which technical rules profit. Australian intervention precedes high trading rule returns, but trading/intervention patterns make it implausible that intervention actually generates those returns. Rather, intervention responds to exchange rate trends from which trading rules have recently profited.Banks and banking, Central ; Foreign exchange ; Trade

Characterisation of FAD-family folds using a machine learning approach

Flavin adenine dinucleotide (FAD) and its derivatives play a crucial role in biological processes. They are major organic cofactors and electron carriers in both enzymatic activities and biochemical pathways. We have analysed the relationships between sequence and structure of FAD-containing proteins using a machine learning approach. Decision trees were generated using the C4.5 algorithm as a means of automatically generating rules from biological databases (TOPS, CATH and PDB). These rules were then used as background knowledge for an ILP system to characterise the four different classes of FAD-family folds classified in Dym and Eisenberg (2001). These FAD-family folds are: glutathione reductase (GR), ferredoxin reductase (FR), p-cresol methylhydroxylase (PCMH) and pyruvate oxidase (PO). Each FADfamily was characterised by a set of rules. The “knowledge patterns” generated from this approach are a set of rules containing conserved sequence motifs, secondary structure sequence elements and folding information. Every rule was then verified using statistical evaluation on the measured significance of each rule. We show that this machine learning approach is capable of learning and discovering interesting patterns from large biological databases and can generate “knowledge patterns” that characterise the FADcontaining proteins, and at the same time classify these proteins into four different families

Consequences of variation in predator attack for the evolution of the selfish herd

There is a strong body of evidence that patterns of collective behaviour in grouping animals are governed by interactions between small numbers of individuals within the group. These findings contrast with study of the ‘selfish herd’, where increasingly complex individual-level movement rules have been proposed to explain the rapid increase in aggregation observed when prey groups are startled by or detect a predator. While individuals using simple rules take into account the position of only a few neighbours, those using complex rules incorporate multiple neighbours, and their relative distance, to determine their movement direction. Here, we simulate the evolution of selfish herd behaviour to assess the conditions under which simple and complex movement rules might evolve, explicitly testing predictions arising from previous work. We find that complex rules outperform simple ones under a range of predator attack strategies, but that simple rules can fix in populations particularly when they are already in the majority, suggesting strong positive frequency dependence in rule success. In addition, we explore whether a movement rule derived from studies of collective behaviour (where individuals use the position of seven neighbours to determine movement direction) performs as successfully as more complex rules, finding again positive frequency dependence in rule success, and a particular role for predator attack strategy (from within or outside the group)

Robust and cost-effective approach for discovering action rules

The main goal of Knowledge Discovery in Databases is to find interesting and usable patterns, meaningful in their domain. Actionable Knowledge Discovery came to existence as a direct respond to the need of finding more usable patterns called actionable patterns. Traditional data mining and algorithms are often confined to deliver frequent patterns and come short for suggesting how to make these patterns actionable. In this scenario the users are expected to act. However, the users are not advised about what to do with delivered patterns in order to make them usable. In this paper, we present an automated approach to focus on not only creating rules but also making the discovered rules actionable. Up to now few works have been reported in this field which lacking incomprehensibility to the user, overlooking the cost and not providing rule generality. Here we attempt to present a method to resolving these issues. In this paper CEARDM method is proposed to discover cost-effective action rules from data. These rules offer some cost-effective changes to transferring low profitable instances to higher profitable ones. We also propose an idea for improving in CEARDM method

Which Learning Algorithms Can Generalize Identity-Based Rules to Novel Inputs?

We propose a novel framework for the analysis of learning algorithms that allows us to say when such algorithms can and cannot generalize certain patterns from training data to test data. In particular we focus on situations where the rule that must be learned concerns two components of a stimulus being identical. We call such a basis for discrimination an identity-based rule. Identity-based rules have proven to be difficult or impossible for certain types of learning algorithms to acquire from limited datasets. This is in contrast to human behaviour on similar tasks. Here we provide a framework for rigorously establishing which learning algorithms will fail at generalizing identity-based rules to novel stimuli. We use this framework to show that such algorithms are unable to generalize identity-based rules to novel inputs unless trained on virtually all possible inputs. We demonstrate these results computationally with a multilayer feedforward neural network.Comment: 6 pages, accepted abstract at COGSCI 201

Evaluation and optimization of frequent association rule based classification

Deriving useful and interesting rules from a data mining system is an essential and important task. Problems such as the discovery of random and coincidental patterns or patterns with no significant values, and the generation of a large volume of rules from a database commonly occur. Works on sustaining the interestingness of rules generated by data mining algorithms are actively and constantly being examined and developed. In this paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided. Empirical results show that with a proper combination of data mining and statistical analysis, the framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy and coverage rules when used in the classification problem. Moreover, the results reveal the important characteristics of mining frequent itemsets, and the impact of confidence measure for the classification task