A new sequential covering strategy for inducing classification rules with ant colony algorithms

Ant colony optimization (ACO) algorithms have been successfully applied to discover a list of classification rules. In general, these algorithms follow a sequential covering strategy, where a single rule is discovered at each iteration of the algorithm in order to build a list of rules. The sequential covering strategy has the drawback of not coping with the problem of rule interaction, i.e., the outcome of a rule affects the rules that can be discovered subsequently since the search space is modified due to the removal of examples covered by previous rules. This paper proposes a new sequential covering strategy for ACO classification algorithms to mitigate the problem of rule interaction, where the order of the rules is implicitly encoded as pheromone values and the search is guided by the quality of a candidate list of rules. Our experiments using 18 publicly available data sets show that the predictive accuracy obtained by a new ACO classification algorithm implementing the proposed sequential covering strategy is statistically significantly higher than the predictive accuracy of state-of-the-art rule induction classification algorithms

Importance-satisfaction analysis for marine-park hinterlands: A Western Australian case study

Tourist use of national and marine parks continues to increase worldwide. Effective management depends on being able to evaluate the quality of visitors' experiences, as well as protecting the natural environment. In tourism management, importance-performance analysis (IPA) has been used as part of quality management. It has recently been applied to national park management. This paper reconceptualises this analysis to one of importance satisfaction, enabling a focus on the quality of experience. Two methods, importanceperformance analysis and service quality gap, were modified and applied in the hinterland of Swan Estuary Marine Park in Western Australia. Both provided data useful for evaluating satisfaction, with the choice of method depending on the end user's resources and requirements as well as cognisance of each method's limitations. For most of the Marine Park attributes, satisfaction exceeded importance and hence no management attention is needed. Exceptions were the condition of the Swan River and associated footpaths, and the presence of litter and wildlife. For these, satisfaction was lower than importance, suggesting management attention is needed

Mining heterogeneous information graph for health status classification

In the medical domain, there exists a large volume of data from multiple sources such as electronic health records, general health examination results, and surveys. The data contain useful information reflecting people’s health and provides great opportunities for studies to improve the quality of healthcare. However, how to mine these data effectively and efficiently still remains a critical challenge. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. By based on analytics of massive data in the National Health and Nutrition Examination Survey, the study builds a classification model to classify patients’health status and reveal the specific disease potentially suffered by the patient. This paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. Moreover, this research contributes to the healthcare community by providing a deep understanding of people’s health with accessibility to the patterns in various observations

Using rule extraction to improve the comprehensibility of predictive models.

Whereas newer machine learning techniques, like artifficial neural net-works and support vector machines, have shown superior performance in various benchmarking studies, the application of these techniques remains largely restricted to research environments. A more widespread adoption of these techniques is foiled by their lack of explanation capability which is required in some application areas, like medical diagnosis or credit scoring. To overcome this restriction, various algorithms have been proposed to extract a meaningful description of the underlying `blackbox' models. These algorithms' dual goal is to mimic the behavior of the black box as closely as possible while at the same time they have to ensure that the extracted description is maximally comprehensible. In this research report, we first develop a formal definition of`rule extraction and comment on the inherent trade-off between accuracy and comprehensibility. Afterwards, we develop a taxonomy by which rule extraction algorithms can be classiffied and discuss some criteria by which these algorithms can be evaluated. Finally, an in-depth review of the most important algorithms is given.This report is concluded by pointing out some general shortcomings of existing techniques and opportunities for future research.Models; Model; Algorithms; Criteria; Opportunities; Research; Learning; Neural networks; Networks; Performance; Benchmarking; Studies; Area; Credit; Credit scoring; Behavior; Time;

Měření averze ke ztrátě soukromého investora

Purpose of the article: This paper gives an empirical view on behaviorance of private investor who is loss averse and whether a loss aversive private investor should invest into such risky assets as equity? The main focus is on the use of robust statistical methods and prospect theory for estimation of equity indexes’ selected characteristics, mainly risk characteristics. The paper contains a detail discussion, which one risk metric for assets seems suitable for private investor who is loss averse. Scientific aim of this article: The aim of the article is a critically describe the problems related with private investor’s loss aversion behaviorance and how the concept of loss aversion should by applied into equities (or equity indices) investment. The crucial problem is how to measure loss aversion of private investor investing in equities. Methodology/methods: The primary and secondary research was applied. Selected scientific articles and other literature published with the topic of prospect theory and risk measurement are mainly used to support a critical analyse of how private investor’s loss aversion should be define and measured in the reality – in the financial/investment area. Next the primary research was done with selected equity indexes. As the representants of equity indexes were chosen not only “typical” representative as MSCI World index but mainly some derivatives of indexes which track a dividend strategy (indexes comprising stocks of companies that pay dividends). Findings: Loss aversive investor worries about any loss of value of their wealth. If these investors choose to invest in stocks they should prefer to invest in the stock indexes with down-side risk close to zero, respectively those indexes whose down-side risk is lowest among all. This down-risk should by measure with using belowtarget semivariance. A standard deviation method as a tool for measurement of risk for loss aversive investor is not so proper due the fact that large positive outcomes are treated as equally risky as large negative ones. In practice, however, positive outliers should be regarded as a bonus and not as a risk. Conclusions: A loss averse investors should some part of his/her wealth invest into equity indexes (may be 15%, max.25%). As the best equity index for a loss adverse investor was chosen Natural Monopoly Index 30 Infrastructure Global with the smallest down side risk

Disclosure measurement in the empirical accounting literature: A review article

This is the first study to provide an extensive and critical review of different techniques used in the empirical accounting literature to measure disclosure. The purpose is to help future researchers to identify exemplars and to select suitable techniques or to develop their own techniques. It also provides in depth discussion of current measurement issues related to disclosure and identifies gaps in the current literature which future research may aim to cover

A sustainable infrastructure delivery model: value added strategy in the Nigerian construction industry

The current economy reforms strategy by the Nigerian government promotes competition among private contractors, which are comprised of local and foreign contractors, in order to achieve value added infrastructure delivery. Resulting competitive bidding processes between multinational construction corporations (MCC) and local construction contractors (LCC) has had mixed comments among stakeholders, with a need for a more sustainable and holistic value approach identified. The aim of this research is to develop a sustainable infrastructure delivery model (SID). The key research methodology is based on extensive literature review and questionnaire survey. SID is developed on the principles and philosophy of soft system methodology (SSM) and analytic network process (ANP). In order to evaluate the significance of MCC and LCC through SID model, questionnaire surveys were conducted. Feedback was collected from experts in the Nigerian construction sector who assessed the relative importance of formulated decision criteria, which were sought under 7 key factors. Data simulation revealed that, through competitive bidding, significant achievements have been made in the delivery of constructed facilities. It was also found that the policy lacked holistic value principles that integrated ethical stance and monetary returns on investment. In this study, SID framework has been presented, clearly showing needs for integration of economic and ethical stances in order to achieve a sustainable infrastructure delivery