Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm

This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. ICET is compared here with three other algorithms for cost-sensitive classification - EG2, CS-ID3, and IDX - and also with C4.5, which classifies without regard to cost. The five algorithms are evaluated empirically on five real-world medical datasets. Three sets of experiments are performed. The first set examines the baseline performance of the five algorithms on the five datasets and establishes that ICET performs significantly better than its competitors. The second set tests the robustness of ICET under a variety of conditions and shows that ICET maintains its advantage. The third set looks at ICET's search in bias space and discovers a way to improve the search.Comment: See http://www.jair.org/ for any accompanying file

A new model of information seeking stopping behavior

textWeb search engines play an important role in peoples daily life. Widespread usage of search engine poses continuous challenges for designing information search systems that can bring people best user experience. To address this challenges, it is particularly important to understand how people seek information. In spite of a large number of studies on human information seeking, the reasons of when and why users terminate information seeking are uncertain and many proposed theories have a limited capability for predicting this type of behavior. In our study, we conducted lab-based experiments, where participants performed assigned information search tasks on Wikipedia pages. Inspired by theories and methods from cognitive science, we captured participants information search behavior such as query usage, search engine result page visits, Wikipedia page visits, and task duration. Additionally, we used eye-tracking techniques to examine the number of people's eye fixations. Using exploratory factor analysis (EFA), we have confirmed exploratory and validation processes can be distinguished based on different types of costs associated with each of them. Based on the findings of the regression tree model, evaluating the cost and gain in the validation process provide important feedback to people for controlling and monitoring their information search.Informatio

A knowledge based system for linking information to support decision making in construction

This work describes the development of a project model centred on the information and knowledge generated and used by managers. It describes a knowledge-based system designed for this purpose. A knowledge acquisition exercise was undertaken to determine the tasks of project managers and the information necessary for and used by these tasks. This information was organised into a knowledge base for use by an expert system. The form of the knowledge lent itself to organisation into a link network. The structure of the knowledge-based system, which was developed, is outlined and its use described. Conclusions are drawn as to the applicability of the model and the final system. The work undertaken shows that it is feasible to benefit from the field of artificial intelligence to develop a project manager assistant computer program that utilises the benefit of information and its link

Searching by approximate personal-name matching

We discuss the design, building and evaluation of a method to access theinformation of a person, using his name as a search key, even if it has deformations. We present a similarity function, the DEA function, based on the probabilities of the edit operations accordingly to the involved letters and their position, and using a variable threshold. The efficacy of DEA is quantitatively evaluated, without human relevance judgments, very superior to the efficacy of known methods. A very efficient approximate search technique for the DEA function is also presented based on a compacted trie-tree structure.Postprint (published version

Massive ontology interface

This paper describes the Massive Ontology Interface (MOI), a web portal which facilitates interaction with a large ontology (over 200,000 concepts and 1.6M assertions) that is built automatically using OpenCyc as a backbone. The aim of the interface is to simplify interaction with the massive amounts of information and guide the user towards understanding the ontology’s data. Using either a text or graph-based representation, users can discuss and edit the ontology. Social elements utilizing gamification techniques are included to encourage users to create and collaborate on stored knowledge as part of a web community. An evaluation by 30 users comparing MOI with OpenCyc’s original interface showed significant improvements in user understanding of the ontology, although full testing of the interface’s social elements lies in the future