Rule pruning in a fuzzy rule-based classification system

In this paper, we purpose a rule pruning strategy to reduce the number of rules in a fuzzy rule-based classification system.A confidence factor, which is formulated based on the compatibility of the rules with the input patterns is under deployed for rule pruning.The pruning strategy aims at reducing the complexity of the fuzzy classification system and, at the same time, maintaining the accuracy rate at a good level.To evaluate the effectiveness of the pruning strategy, two benchmark data sets are first tested. Then, a fault classification problem with real senor measurements collected from a power generation plant is evaluated.The results obtained are analyzed and explained, and implications of the proposed rule pruning strategy to the fuzzy classification system are discussed.

The application of a fuzzy rule based system in an exposition of the antecedents of sedge warbler song flight

The utilization of a fuzzy aspect within data analysis attempts to move from a quantitative to a more qualitative investigative environment. As such, this may allow the more non-quantitative researchers results they can use, based on sets of linguistic terms. In this paper an inductive fuzzy decision tree approach is utilized to construct a fuzzy-rule-based system for the first time in a biological setting. The specific biological problem considered attempts to identify the antecedents (conditions in the fuzzy decision rules) which characterize the length of song flight of the male sedge warbler when attempting to attract a mate. Hence, for a non-quantitative investigator the resultant set of fuzzy rules allows an insight into the linguistic interpretation on the relationship between associated characteristics and the respective song flight duration.

Measuring moral judgement and the implications of cooperative education and rule-based learning

The Defining Issues Test (DIT), developed by Rest (1986), measures a person's level of moral development using hypothetical social dilemmas. Although the DIT is useful for measuring moral development in social settings, it might not adequately capture an individual's moral judgement abilities in solving work-related problems (Weber, 1990; Trevino, 1992; Welton et al., 1994). In the present study, the moral judgement levels of 97 accounting students were measured over a 1 year period using two separate test instruments, the DIT and a context-specific instrument developed by Welton et al. (1994). The test scores are significantly higher on the DIT than the Welton instrument (between the instruments and over time), suggesting that accounting students use higher levels of moral reasoning in resolving hypothetical social dilemmas and lower levels of moral reasoning in resolving context-specific dilemmas. The difference in test scores was highest during cooperative education (work placement programme), implying that the environment is a significant determinant on students' test scores.

Rule-based dependency models for security protocol analysis

Security protocol analysis has been discussed for quite some time in the past few years. Although formal methods have been widely used to identify various vulnerabilities, mainly susceptibility to freshness attacks and impersonation, the arisen inconsistent data between principals and collusion attacks held by a group of dishonest principals have been largely ignored. Moreover, the previous methods focus on reasoning about certain security-related properties and detecting known attacks against secure message, whereas there have been insufficient efforts to handle the above hidden but powerful attacks. In this paper, we address these critical issues and prove the efficiency and intuitiveness of rule-based dependency models in defending a protocol against the attacks. This is able to provide a numerical estimation to measure he occurrence of these attacks. It will be useful in enhancing the current protocol analysis.

A rule-based intelligent energy management system for an internal combustion engine vehicle

An intelligent energy management system (IEMS) is developed to improve fuel efficiency of an internal combustion engine vehicle. It helps determine the best approach to run the engine system through dynamically analysing various factors relating to vehicle. The energy balance technique is implemented and utilised. The simulation outcome of the IEMS is compared against that of a conventional system under the same driving factors. The results show that the IEMS reduces the fuel consumption around 5.6% for the tested conditions.

Potential applications of discrete-event simulation and fuzzy rule-based systems to structural reliability and availability

This chapter discusses and illustrates some potential applications of discrete-event simulation (DES) techniques in structural reliability and availability analysis, emphasizing the convenience of using probabilistic approaches in modern building and civil engineering practices. After reviewing existing literature on the topic, some advantages of probabilistic techniques over analytical ones are highlighted. Then, we introduce a general framework for performing structural reliability and availability analysis through DES. Our methodology proposes the use of statistical distributions and techniques – such as survival analysis – to model component-level reliability. Then, using failure- and repair-time distributions and information about the structural logical topology (which allows determination of the structural state from their components’ state), structural reliability, and availability information can be inferred. Two numerical examples illustrate some potential applications of the proposed methodology to achieving more reliable and structural designs. Finally, an alternative approach to model uncertainty at component level is also introduced as ongoing work. This new approach is based on the use of fuzzy rule-based systems and it allows the introduction of experts’ opinions and evaluations in our methodology.

A new monotonicity index for fuzzy rule-based systems

A search in the literature reveals that mathematical conditions (usually sufficient conditions) for the Fuzzy Inference System (FIS) models to satisfy the monotonicity property have been developed. A monotonically-ordered fuzzy rule base is important to maintain the monotonicity property of an FIS. However, it may difficult to obtain a monotonically-ordered fuzzy rule base in practice. We have previously introduced the idea of fuzzy rule relabeling to tackle this problem. In this paper, we further propose a monotonicity index for the FIS system, which serves as a metric to indicate the degree of a fuzzy rule base fulfilling the monotonicity property. The index is useful to provide an indication whether a fuzzy rule base should (or should not) be used in practice, even with fuzzy rule relabeling. To illustrate the idea, the zero-order Sugeno FIS model is exemplified. We add noise as errors into the fuzzy rule base to formulate a set of non-monotone fuzzy rules. As such, the metric also acts as a measure of noise in the fuzzy rule base. The results show that the proposed metric is useful to indicate the degree of a fuzzy rule base fulfilling the monotonicity property.

Rule-based extraction of goal-use case models from text

Goal and use case modeling has been recognized as a key approach for understanding and analyzing requirements. However, in practice, goals and use cases are often buried among other content in requirements specifications documents and written in unstructured styles. It is thus a time-consuming and error-prone process to identify such goals and use cases. In addition, having them embedded in natural language documents greatly limits the possibility of formally analyzing the requirements for problems. To address these issues, we have developed a novel rule-based approach to automatically extract goal and use case models from natural language requirements documents. Our approach is able to automatically categorize goals and ensure they are properly specified. We also provide automated semantic parameterization of artifact textual specifications to promote further analysis on the extracted goal-use case models. Our approach achieves 85% precision and 82% recall rates on average for model extraction and 88% accuracy for the automated parameterization.

Microarray data classification using automatic SVM kernel selection

Microarray data classification is one of the most important emerging clinical applications in the medical community. Machine learning algorithms are most frequently used to complete this task. We selected one of the state-of-the-art kernel-based algorithms, the support vector machine (SVM), to classify microarray data. As a large number of kernels are available, a significant research question is what is the best kernel for patient diagnosis based on microarray data classification using SVM? We first suggest three solutions based on data visualization and quantitative measures. Different types of microarray problems then test the proposed solutions. Finally, we found that the rule-based approach is most useful for automatic kernel selection for SVM to classify microarray data.

Evaluating and improving morpho-syntactic classification over multiple corpora using pre-trained, "off-the-shelf", parts-of-speech tagging tools

This paper evaluates six commonly available parts-of-speech tagging tools over corpora other than those upon which they were originally trained. In particular this investigation measures the performance of the selected tools over varying styles and genres of text without retraining, under the assumption that domain specific training data is not always available. An investigation is performed to determine whether improved results can be achieved by combining the set of tagging tools into ensembles that use voting schemes to determine the best tag for each word. It is found that while accuracy drops due to non-domain specific training, and tag-mapping between corpora, accuracy remains very high, with the support vector machine-based tagger, and the decision tree-based tagger performing best over different corpora. It is also found that an ensemble containing a support vector machine-based tagger, a probabilistic tagger, a decision-tree based tagger and a rule-based tagger produces the largest increase in accuracy and the largest reduction in error across different corpora, using the Precision-Recall voting scheme.

Evaluating and improving morpho-syntactic classification over multiple corpora using pre-trained, “off-the-shelf”, parts-of-speech tagging tools

his paper evaluates six commonly available parts-of-speech tagging tools over corpora other than those upon which they were originally trained. In particular this investigation measures the performance of the selected tools over varying styles and genres of text without retraining, under the assumption that domain specific training data is not always available. An investigation is performed to determine whether improved results can be achieved by combining the set of tagging tools into ensembles that use voting schemes to determine the best tag for each word. It is found that while accuracy drops due to non-domain specific training, and tag-mapping between corpora, accuracy remains very high, with the support vector machine-based tagger, and the decision tree-based tagger performing best over different corpora. It is also found that an ensemble containing a support vector machine-based tagger, a probabilistic tagger, a decision-tree based tagger and a rule-based tagger produces the largest increase in accuracy and the largest reduction in error across different corpora, using the Precision-Recall voting scheme.

Anisotropic hardening model based on non-associated flow rule and combined nonlinear kinematic hardening for sheet materials

 A material model for more effective analysis of plastic deformation of sheet materials is presented in this paper. The model is capable of considering the following aspects of plastic deformation behavior of sheet materials: the anisotropy in yielding stresses in different directions by using a quadratic yield function (based on Hill’s 1948 model and stress ratios), the anisotropy in work hardening by introducing non-constant flow stress hardening in different directions, the anisotropy in plastic strains in different directions by using a quadratic plastic potential function and non-associated flow rule (based on Hill’s 1948 model and plastic strain ratios, r-values), and finally some of the cyclic hardening phenomena such as Bauschinger’s effect and transient behavior for reverse loading by using a coupled nonlinear kinematic hardening (so-called Armstrong-Frederick-Chaboche model). Basic fundamentals of the plasticity of the model are presented in a general framework. Then, the model adjustment procedure is derived for the plasticity formulations. Also, a generic numerical stress integration procedure is developed based on backward-Euler method (so-called multistage return mapping algorithm). Different aspects of the model are verified for DP600 steel sheet. Results show that the new model is able to predict the sheet material behavior in both anisotropic hardening and cyclic hardening regimes more accurately. By featuring the above-mentioned facts in the presented constitutive model, it is expected that more accurate results can be obtained by implementing this model in computational simulations of sheet material forming processes. For instance, more precise results of springback prediction of the parts formed from highly anisotropic hardened materials or that of determining the forming limit diagrams is highly expected by using the developed material model.

How well do ecosystem-based planning units represent different componenets of biodiversity?

There are many proposals for managing biodiversity by using surrogates, such as umbrella, indicator, focal, and flagship species. We use the term biodiversity management unit for any ecosystem-based classificatory scheme for managing biodiversity. The sufficiency of biodiversity management unit classification schemes depends upon (1) whether different biotic elements (e.g., trees, birds, reptiles) distinguish between biodiversity management units within a classification (i.e., coherence within classes}; and (2) whether different biotic elements agree upon similarities and dissimilarities among biodiversity management unit classes (i.e., conformance among classes). Recent evaluations suggest that biodiversity surrogates based on few or single taxa are not useful. Ecological vegetation classes are an ecosystem-based classification scheme used as one component for biodiversity management in Victoria, Australia. Here we evaluated the potential for ecological vegetation classes to be used as biodiversity management units in the box-ironbark ecosystem of central Victoria, Australia. Eighty sites distributed among 14 ecological vegetation classes were surveyed in the same ways for tree species, birds, mammals, reptiles, terrestrial invertebrates, and nocturnal flying insects. Habitat structure and geographic separations also were measured, which, with the biotic elements, are collectively referred to as variables. Less than half of the biotic element-ecological vegetation class pairings were coherent. Generalized Mantel tests were used to examine conformance among variables with respect to ecological vegetation classes. While most tests were not significant, birds, mammals, tree species, and habitat structure together showed significant agreement on the rating of similarities among ecological vegetation classes. In this system, use of ecological vegetation classes as biodiversity management units may account reasonably well for birds, mammals, and trees; but reptiles and invertebrates would not be accommodated. We conclude that surrogates will usually have to be augmented or developed as hierarchies to provide general representativeness.