The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified.

Concept drift refers to a non stationary learning problem over time. The training and the application data often mismatch in real life problems. In this report we present a context of concept drift problem 1. We focus on the issues relevant to adaptive training set formation. We present the framework and terminology, and formulate a global picture of concept drift learners design. We start with formalizing the framework for the concept drifting data in Section 1. In Section 2 we discuss the adaptivity mechanisms of the concept drift learners. In Section 3 we overview the principle mechanisms of concept drift learners. In this chapter we give a general picture of the available algorithms and categorize them based on their properties. Section 5 discusses the related research fields and Section 5 groups and presents major concept drift applications. This report is intended to give a bird's view of concept drift research field, provide a context of the research and position it within broad spectrum of research fields and applications.

Model-based diagnostic reasoning often leads to a large number of diagnostic hypotheses. The set of diagnoses can be reduced by taking into account extra observations (passive monitoring), measuring additional variables (probing) or executing additional tests (sequential diagnosis/test sequencing). In this paper we combine the above approaches with techniques from Automated Test Pattern Generation (ATPG) and Model-Based Diagnosis (MBD) into a framework called FRACTAL (FRamework for ACtive Testing ALgorithms). Apart from the inputs and outputs that connect a system to its environment, in active testing we consider additional input variables to which a sequence of test vectors can be supplied. We address the computationally hard problem of computing optimal control assignments (as defined in FRACTAL) in terms of a greedy approximation algorithm called FRACTAL-G. We compare the decrease in the number of remaining minimal cardinality diagnoses of FRACTAL-G to that of two more FRACTAL algorithms: FRACTAL-ATPG and FRACTAL-P. FRACTAL-ATPG is based on ATPG and sequential diagnosis while FRACTAL-P is based on probing and, although not an active testing algorithm, provides a baseline for comparing the lower bound on the number of reachable diagnoses for the FRACTAL algorithms. We empirically evaluate the trade-offs of the three FRACTAL algorithms by performing extensive experimentation on the ISCAS85/74XXX benchmark of combinational circuits.

We propose a StochAstic Fault diagnosis AlgoRIthm, called SAFARI, which trades off guarantees of computing minimal diagnoses for computational efficiency. We empirically demonstrate, using the 74XXX and ISCAS-85 suites of benchmark combinatorial circuits, that SAFARI achieves several orders-of-magnitude speedup over two well-known deterministic algorithms, CDA* and HA*, for multiple-fault diagnoses; further, SAFARI can compute a range of multiple-fault diagnoses that CDA* and HA* cannot. We also prove that SAFARI is optimal for a range of propositional fault models, such as the widely-used weak-fault models (models with ignorance of abnormal behavior). We discuss the optimality of SAFARI in a class of strong-fault circuit models with stuck-at failure modes. By modeling the algorithm itself as a Markov chain, we provide exact bounds on the minimality of the diagnosis computed. SAFARI also displays strong anytime behavior, and will return a diagnosis after any non-trivial inference time.

Galles and Pearl [1998] claimed that ``for recursive models, the causal model framework does not add any restrictions to counterfactuals, beyond those imposed by Lewis's [possible-worlds] framework.''  This claim is shown to be false.  Indeed, the opposite claim is true: recursive models are shown to correspond precisely to a subclass of (possible-world) counterfactual structures.  On the other hand, a slight generalization of recursive models, models where all equations have unique solutions, is shown to be incomparable in expressive power to counterfactual structures, despite the fact that the Galles and Pearl arguments should apply to them as well.  The problem with the Galles and Pearl argument is identified: an axiom that they viewed as irrelevant, because it involved disjunction (which was not in their language), is not irrelevant at all. 

In discrete dynamical systems change results from actions. As such, given a set of observations, diagnoses often take the form of posited events that result in the observed behaviour. In this paper we revisit formal characterizations of diagnosis, and their relationship to planning. We do so from both a theoretical and a computational perspective. In particular, we extend the characterization of diagnosis to deal with the case of incomplete information, and rich preferences. We also explore the use of state-of-the-art planning technology for the automated generation of diagnoses. Examining several classes of diagnosis problems, we provide both proof of concept and benchmark experiments, the latter showing superior performance to a leading diagnosis engine. Our findings help support the hypothesis that planning technology holds great promise for efficient generation of diagnoses.

Sequential diagnosis takes measurements of an abnormal system to identify faulty components, where the goal is to reduce the diagnostic cost , defined here as the number of measurements. To propose measurement points, previous work employs a heuristic based on reducing the entropy over a set of diagnoses , which can be impractical when the set of diagnoses is too large. Focusing on a smaller set of probable diagnoses scales the approach but generally leads to increased diagnostic cost. We propose a new diagnostic framework employing three new techniques — a more efficient heuristic for measurement point selection, abstraction-based sequential diagnosis, and component cloning — which scales to large systems with good performance in terms of diagnostic cost.

This paper extends model-based diagnosis (MBD) (de Kleer and Williams 1987; Reiter 1987) to systems with hidden interaction faults. An interaction fault is present if an interaction among a set of components leads to an observable failure, even though each individual component individually meets the specifications. A naive approach to address interaction faults is to simply account for all possible interaction faults in the system model. However, the naive approach presumes that all possible faults, both component and interaction faults, are known and addressed in the model. This assumption is violated by most real world systems, such as shorts in circuits (Davis 1984) or unmodeled connections (de Kleer 2007). That leads to incomplete system models, hence possibly hidden interaction faults. The problem of hidden interactions has been known for a long time (Davis 1984), but until now no general solution has been proposed. Instead of pushing for complete models (Preist and Welham 1990) or relying on additional structural information (Davis 1984; Bottcher 1995; de Kleer 2007) we approach the challenge differently. We allow system models to be incomplete and introduce a general, domain independent extension to model-based diagnosis to account for resulting hidden interaction faults. This extends model-based diagnosis to systems with incomplete models, in particular to models with incomplete structural information. In the paper, we demonstrate the proposed diagnosis framework on a logic circuit with a hidden interaction fault.

Virtual teams that span multiple geographic and cultural boundaries have become commonplace in numerous organizations due to the competitive advantages they provide in human resources, products, financial means, knowledge sharing and many others. However, the promises of multinational and multicultural (MNMC) distributed teams are accompanied by a number of challenges. Many research studies have suggested that one of the most challenging barriers to the effective implementation of MNMC distributed teams is culture. In this study, data collected from the experiment conducted by the NATO RTO Human Factors and Medicine Panel Research Task Group (HFM-138/RTG) on “Adapatability in Multinational Coalitions” has been analyzed to study the effects of cultural diversity on team planning and communication. Fuzzy decision trees have been derived to model the effects, and information visualization techniques are used to facilitate understanding of the derived classification patterns. Results of the research suggest that there are no single and straightforward conclusions on how cultural diversity affects team planning and communication. Different dimensions of culture values interact in influencing team behaviors. However, diversities in power distance and masculinity seem to play more influential roles than others.

The failure diagnosis for {\it discrete event systems} (DESs) has been given considerable attention in recent years. Both on-line and off-line diagnostics in the framework of DESs was first considered by Lin Feng in 1994, and particularly an algorithm for diagnosability of DESs was presented. Motivated by some existing problems to be overcome in previous work, in this paper, we investigate the minimal cost algorithm for diagnosability of DESs. More specifically: (i) we give a generic method for judging a system's off-line diagnosability, and the complexity of this algorithm is polynomial-time; (ii) and in particular, we present an algorithm of how to search for the minimal set in all observable event sets, whereas the previous algorithm may find {\it non-minimal} one.