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This paper deals with the multiple-rule problem which arises when several decision rules (of different classes) match ("fire" for) an input to-be-classified (unseen) object. The paper focuses on formal aspects and theoretical methodology for the above problem.

The general definitions of the notions of a Designer, Learner and Classifier are presented in a formal matter, including parameters that are usually attached to the above concepts such as rule consistency, completeness, quality, matching rate, etc. We thus provide the minimum-requirement definitions as necessary conditions for these concepts. Any designer (decision-system builder) of a new multiple-rule system may start with these minimum requirements.

We only expect that the Classifier makes its decisions according to its decision scheme induced as a knowledge base (theory, model, concept description). Also, two case studies are discussed. We conclude with a general flow chart for a decision-system builder. He/she can just pursue it and select parameters of a Learner and Classifier, following the minimum requirements provided.

In attempting to address real-life decision problems, where uncertainty about data prevails, some kind of representation of imprecise information is important and several have been proposed. In particular, first-order representations, such as sets of probability measures, upper and lower probabilities, and interval probabilities and utilities of various kinds, have been suggested for enabling a better representation of the input sentences for a subsequent decision analysis. However, sometimes second-order approaches are better suited for modelling incomplete knowledge and we demonstrate how such can add important information when handling aggregations of imprecise representations, as is the case in decision trees or probabilistic networks. Based on this, we suggest a measure of belief density for such intervals. We also demonstrate important properties when operating on general distributions. The results equally apply to approaches which do not explicitly deal with second-order distributions, instead using only first-order concepts such as upper and lower bounds. While the discussion focuses on probabilistic decision trees, the results apply to other formalisms involving products of probabilities, such as probabilistic networks, and to formalisms dealing with products of interval entities such as interval weight trees in multi-criteria decision making.

Deep parsing of Chinese sentences is a very challenging task due to their complexity such as ambiguous word boundaries and meanings. An alternative mode of Chinese language processing is to perform shallow parsing of Chinese sentences in which chunk segmentation plays an important role. In this paper, we present a chunk segmentation algorithm using a combined statistical and rule-based approach (CSRA). The decision rules for refining chunk segmentation are generated from incorrectly segmented chunks from a statistical model which is built on a training corpus. Experimental results show that the CSRA works well and produces satisfactory chunk segmentation results for subsequent processes such as chunk tagging and chunk collocation extraction.

The problems of the nature of expertise and the possibility to create a copy of expert knowledge in the computer are the main themes for the paper. In the first part of this paper, a short survey of existing knowledge about expert behavior is given. The new method for the construction of exact copy of expert knowledge-CONSER is described. The utilization of new method gives new information about the striking ability of expert to recognize and classify the situations on the basis of their knowledge.

We present a method for She classification of cancer based on gene expression profiles using single genes. We select the genes with high class-discrimination capability according to their depended degree by the classes. We then build classifiers based on the decision rules induced by single genes selected. We test our single-gene classification method on three publicly available cancerous gene expression datasets. In a majority of cases, we gain relatively accurate classification outcomes by just utilizing one gene. Some genes highly correlated with the pathogenesis of cancer are identified. Our feature selection and classification approaches are both based on rough sets, a machine learning method. In comparison with other methods, our method is simple, effective and robust. We conclude that, if gene selection is implemented reasonably, accurate molecular classification of cancer can be achieved with very simple predictive models based on gene expression profiles.

This paper introduces an application of a combination of approximate time windows, the Choquet integral, and rough sets in the design of a software deployability control system. A multi-criteria evaluation of coalitions of software development technologies is considered in approximate reasoning about the deployability of software products. Measurements with the Choquet integral are incorporated into a feedback system used to control software deployability. Decision rules capture dependencies between function points, project duration requirement, aggregation of granulations of multi-criteria evaluations of software development technologies with the Choquet integral, and magnitude of relative error in cost estimation. The method of rule derivation comes from rough sets. A fuzzy measure model of the importance of various coalitions of software technologies is introduced. The approximate character of the reasoning stems from the inherent imprecision of measurements of processing complexity. The approach is illustrated relative to software cost estimates for a collection of twenty-five software projects. The contribution of this paper is a formal description of an approximate time software deployability feedback control system based on an approximate reasoning approach to assessing software cost and software quality.