Inductive learning that creates a decision tree from a set of existing examples is shown to be useful for automated knowledge acquisition. Most of the existing methods however, handle only single-dimensional decision problems. Only some methods can deal with multi-dimensional decision problems. However, they are based on crisp concepts that are weak in handling marginal cases. In this paper, we present a multi-dimensional fuzzy inductive learning method that integrates the fuzzy set theory into the conventional multi-dimensional decision tree induction methods. The method converts a multi-dimensional decision tree into a fuzzy multi-dimensional decision tree in which hurdle values for splitting branches and classes associated with leaves are fuzzy. Results from empirical tests indicate that the new fuzzy approach outperforms the other conventional methods.

This paper is about using intelligent agent technology to facilitate the C2B (consumer-to-business) business model on Internet. The benefits of C2B, a collective purchasing model, are obvious, but there are some unresolved problems that obstruct the growth of this model. For examples, how to gather together individual's preferences and translate them into a groups synthesis? What is the mechanism to interact with each other in the virtual group, and how does the group negotiate with the sellers? In order to resolve these difficulties, we have proposed a multi-agent framework to support the C2B model in electronic commerce, and the new algorithm - adaptive-AHP has been proposed. Finally, we have implemented a prototype system on .NET for notebook computer purchasing to show how the adaptive-AHP is applied in the multi-agent systems.

Data flow and domain testing are both well-known program-based testing techniques. Each has its advantages and weaknesses. This paper presents an integrated approach that inherits the strengths of the two testing techniques but without their deficiencies.>

article Free Access Share on A simplified domain-testing strategy Authors: Bingchiang Jeng Sun Yat-Sen University Sun Yat-Sen UniversityView Profile , Elaine J. Weyuker New York University New York UniversityView Profile Authors Info & Claims ACM Transactions on Software Engineering and MethodologyVolume 3Issue 3July 1994 pp 254–270https://doi.org/10.1145/196092.193171Published:01 July 1994Publication History 49citation1,628DownloadsMetricsTotal Citations49Total Downloads1,628Last 12 Months60Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Partition testing strategies, which divide a program's input domain into subsets with the tester selecting one or more elements from each subdomain, are analyzed. The conditions that affect the efficiency of partition testing are investigated, and comparisons of the fault detection capabilities of partition testing and random testing are made. The effects of subdomain modifications on partition testing's ability to detect faults are studied.>