Description:
While any input may be contributing, imprecise specification of class of data subdivided into classes identifies as rather common a source of noise. The misrepresentation may be characteristic of the data or be caused by forcing of a regression problem into the classification type. Consideration is given to examples of this nature, and an alternative is proposed. In the main part, the approach is based on a well-known technique of data treatment for noise using k-NN. The paper advances an editing technique designed around idea of variable number of authenticating instances. Test runs performed on publicly available and proprietary data demonstrate high retention ability of the new procedure without loss of classification accuracy. Noise reduction methods in a broader classification context are extensively surveyed.
Description:
Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature.
Description:
Technology currently encourages the capture and storage of vast quantities of data and information and so thinkers, reasoners, and decision-makers have available large resources to support their tasks. At the same time, there is a need to engage with an enormous range of complex issues that require reasoning and decisions that are actionable to address them. Approaches for Community Decision Making and Collective Reasoning: Knowledge Technology Support acts to provide knowledge for each individual in a group with the broad structural wealth of reasoning. It also acts as an explicit structure that technological devices for supporting reasoning within a group can hook onto. If you are interested in how groups can structure their activities towards making better decisions or in developing technologies for the support of decision-making in groups, then this book is an excellent way to understand the state of the art and possible ways forward.
Description:
It is well known that classification models produced by the Ripple Down Rules are easier to maintain and update. They are compact and can provide an explanation of their reasoning making them easy to understand for medical practitioners. This article is devoted to an empirical investigation and comparison of several ensemble methods based on Ripple Down Rules in a novel application for the detection of cardiovascular autonomic neuropathy (CAN) from an extensive data set collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments included essential ensemble methods, several more recent state-of-the-art techniques, and a novel consensus function based on graph partitioning. The results show that our novel application of Ripple Down Rules in ensemble classifiers for the detection of CAN achieved better performance parameters compared with the outcomes obtained previously in the literature.
Description:
This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. First, we carried out a thorough investigation comparing the performance of various base classifiers for several known sets of the most essential features in this database and determined that Random Forest significantly and consistently outperforms all other base classifiers in this new application. Second, we used feature selection and ranking implemented in Random Forest. It was able to identify a new set of features, which has turned out better than all other sets considered for this large and well-known database previously. Random Forest remained the very best classifier for the new set of features too. Third, we investigated meta classifiers and new multi-level meta classifiers based on Random Forest, which have improved its performance. The results obtained show that novel multi-level meta classifiers achieved further improvement and obtained new outcomes that are significantly better compared with the outcomes published in the literature previously for cardiac autonomic neuropathy.
Description:
The central theme of this chapter is that the application of machine learning to data in the legal domain involves considerations that derive from jurisprudential assumptions about the nature of legal reasoning. Jurisprudence provides a unique resource for machine learning in that, for over one hundred years, significant thinkers have advanced concepts including open texture and discretion. These concepts inform and guide applications of machine learning to law.
Description:
This paper proposes TEA: a generic framework for decision making in web services, which integrates the environment (6 Ps) of decision making, the behaviors (6 Cs) of decision makers, and inner activities (another 6 Ps) of decision makers. This framework unifies what the decision makers can "eye" (the above-mentioned first 6Ps), should "think" (the above-mentioned another 6 Ps) and "act" (6 Cs), whenever making decisions in web services. The paper also examines interrelationships among the first 6 Ps, 6 Cs, and another 6Ps, and their influences on decision making in web services. The proposed approach will facilitate research and development of decision making and decision support systems in web services.
Description:
Recent trends in the increasing use of complementary and alternative medicine (CAM) as "holistic medicine" by patients in technologically advanced nations have prompted the need to integrate their CAM information into their Electronic health records (EHR). Studies indicate that over 70% of the public in Australia used at least one form of CAM that includes nutritional products such as vitamins, supplements, and herbal medicines, and alternate medicines such as homoeopathic, Ayurvedic and Chinese medicines. There is also a growing acceptance of CAM among healthcare providers, and patients are increasingly visiting CAM practitioners. In this paper, we argue that by unifying patients' information about their CAM history along with their EHR, the healthcare quality and accuracy of measurements could be improved, and we identify six key benefits for healthcare and CAM practitioners as well as consumers. On the other hand we also foresee certain issues, such as availability of electronic data and standardised practice of different forms of CAM, and we have unearthed six main issues that require prime attention. We discuss these issues and provide recommendations for the way to go forward in integrating automated CAM software components into EHR systems.
Description:
Recent trends in the increasing use of complementary and alternative medicine (CAM) as "holistic medicine" by patients in technologically advanced nations have prompted the need to integrate their CAM information into their Electronic health records (EHR). Studies indicate that over 70% of the public in Australia used at least one form of CAM that includes nutritional products such as vitamins, supplements, and herbal medicines, and alternate medicines such as homoeopathic, Ayurvedic and Chinese medicines. There is also a growing acceptance of CAM among healthcare providers, and patients are increasingly visiting CAM practitioners. In this paper, we argue that by unifying patients' information about their CAM history along with their EHR, the healthcare quality and accuracy of measurements could be improved, and we identify six key benefits for healthcare and CAM practitioners as well as consumers. On the other hand we also foresee certain issues, such as availability of electronic data and standardised practice of different forms of CAM, and we have unearthed six main issues that require prime attention. We discuss these issues and provide recommendations for the way to go forward in integrating automated CAM software components into EHR systems.
Description:
In software engineering, the re-use concept is a design principle that improves efficency, quality and maintainability by ensuring that software artifacts are developed once and re-used may times. In an analogous way, a group's reasoning can be imagined to be re-used by that or another group to enhance efficiency, transparency and consistency in decison-making. However, the re-use of reasoning is difficult to achieve because group reasoning cannot easily be captured and the way in which a group reasoning artifact is subsequently used is not obvious. This chapter explores the case for the re-use of community reasoning and concludes that individuals can benefit from a representation of a previous groups's coalesced reasoning to be modeled and the scheme to represent the reasoning have been selected to suit the task. The authors contend that specifying the future community like to re-use the reasoning, called the intended audience, informs a decision regarding whether an exercise aimed at coalescing a group's reasoning is best performed verbally, in writing or with the use of more structured schemes such as Argument visualization.
Description:
This chapter describes some of the current approaches to delibertative democracy and the considers them from the perspective of a reasoning community framework. This approach highlights important tasks, process and structures that can be used to enhance the process of groups engaging in deliberative democracy approaches. In particular it focuses attention on the potential for technologies to support groups in achieving broad agreed structured reasoning bases that capture the scope of an issue from multiple perspectives.