When applying estimation methods, the issue of outliers is inevitable. The extent of their influence has not been clarified, though several studies have evaluated outlier elimination methods. It is unclear whether we should always be sensitive to outliers, whether outliers should always be removed before estimation, and what amount of precaution is required for collecting project data. Therefore, the goal of this study is to illustrate a guideline that suggests how sensitively we should handle outliers. In the analysis, we experimentally add outliers to three datasets, to analyze their influence. We modified the percentage of outliers, their extent (e.g., we varied the actual effort from 100 to 200 person-hours when the extent was 100%), the variables including outliers (e.g., adding outliers to function points or effort), and the locations of outliers in a dataset. Next, the effort was estimated using these datasets. We used multiple linear regression analysis and analogy based estimation to estimate the development effort. The experimental results indicate that the influence of outliers on the estimation accuracy is non-trivial when the extent or percentage of outliers is considerable (i.e., 100% and 20%, respectively). In contrast, their influence is negligible when the extent and percentage are small (i.e., 50% and 10%, respectively). Moreover, in some cases, the linear regression analysis was less affected by outliers than analogy based estimation.

Recommender systems have attracted attention in both the academic and the business areas. They aim to give users more intelligent methods for navigating and identifying complex information spaces, especially in e-commerce domain. However, these systems still have to overcome certain limitations that reduce their performance, such as overspecialization of recommendations, cold-start, and difficulties when items with unequal probability distribution exist. A novel approach addresses the above issues through a case-based recommendation methodology which is a form of content-based recommendation that is well suited to many product recommendation domains, owing to the clear organization of users' needs and preferences. Unfortunately, the experience-based roots of case-based reasoning are not clearly reflected in case-based recommenders. In other words, the concept that product cases, which are usually fixed feature-based tuples, are experiential is not adopted well in case-based recommenders. To solve this problem as well as the recommenders' rating sparsity issue, one can use product reviews which are generated from users' experience with the product a basis of product information. Our approach adapts the use of sentiment scores along with feature similarity throughout the recommendation unlike traditional case-based recommender systems, which tend to depend entirely on pure similarity-based approaches. This paper models product cases with the products' features and sentiment scores at the feature level and product level. Thus, combining user experience and similarity measures improves the recommender performance and gives users more flexibility to choose whether they prefer products more similar to their query or better qualified products. We present the results using different evaluation methods for different case structures, different numbers of similar cases retrieved and multilevel sentiment-approaches. The recommender performance was highly improved with the use of feature-level sentiment approach, which recommends product cases that are similar to the query but favored for customers.

Product return is a critical but controversial issue. To deal with such a vague return problem, businesses must improve their information transparency in order to administrate the product return behaviour of their end users. This study proposes an intelligent return administration expert system (iRAES) to provide product return forecasting and decision support for returned product administration. The iRAES consists of two intelligent agents that adopt a hybrid data mining algorithm. The return diagnosis agent generates different alarms for certain types of product return, based on forecasts of the return possibility. The return recommender agent is implemented on the basis of case-based reasoning, and provides the return centre clerk with a recommendation for returned product administration. We present a 3C-iShop scenario to demonstrate the feasibility and efficiency of the iRAES architecture. Our experiments identify a particularly interesting return, for which iRAES generates a recommendation for returned product administration. On average, iRAES decreases the effort required to generate a recommendation by 70% compared to previous return administration systems, and improves performance via return decision support by 37%. iRAES is designed to accelerate product return administration, and improve the performance of product return knowledge management.

Inspired from natural self-managing behavior of the human body, autonomic systems promise to inject self-managing behavior in software systems. Such behavior enables self-configuration, self-healing, self-optimization and self-protection capabilities in software systems. Self-configuration is required in systems where efficiency is the key issue, such as real time execution environments. To solve self-configuration problems in autonomic systems, the use of various problem-solving techniques has been reported in the literature including case-based reasoning. The case-based reasoning approach exploits past experience that can be helpful in achieving autonomic capabilities. The learning process improves as more experience is added in the case-base in the form of cases. This results in a larger case-base. A larger case-base reduces the efficiency in terms of computational cost. To overcome this efficiency problem, this paper suggests to cluster the case-base, subsequent to find the solution of the reported problem. This approach reduces the search complexity by confining a new case to a relevant cluster in the case-base. Clustering the case-base is a one-time process and does not need to be repeated regularly. The proposed approach presented in this paper has been outlined in the form of a new clustered CBR framework. The proposed framework has been evaluated on a simulation of Autonomic Forest Fire Application (AFFA). This paper presents an outline of the simulated AFFA and results on three different clustering algorithms for clustering the case-base in the proposed framework. The comparison of performance of the conventional CBR approach and clustered CBR approach has been presented in terms of their Accuracy, Recall and Precision (ARP) and computational efficiency.

Finding intracranial aneurysms plays a key role in preventing serious cerebral diseases such as subarachnoid hemorrhage. For detection of aneurysms, magnetic resonance angiography (MRA) can provide detailed images of arteries non-invasively. However, because over 100 MRA images per subject are required to cover the entire cerebrum, image diagnosis using MRA is very time-consuming and labor-intensive. This article presents a computer-aided diagnosis (CAD) system for finding aneurysms with MRA images. The principal components are identification of aneurysm candidates (= ROIs; regions of interest) from MRA images and estimation of a fuzzy degree for each aneurysm candidate based on a case-based reasoning (CBR). The fuzzy degree indicates whether a candidate is true aneurysm. Our system presents users with a limited number of ROIs that have been sorted in order of fuzzy degree. Thus, this system can decrease the time and the labor required for detecting aneurysms. Experimental results using phantoms indicate that the system can detect all aneurysms at branches of arteries and all saccular aneurysms produced by dilation of a straight artery in 1 direction perpendicular to the principal axis. In a clinical evaluation, performance in finding aneurysms and estimating the fuzzy degree was examined by applying the system to 16 subjects with a total of 19 aneurysms. The experimental results indicate that this CAD system detected all aneurysms except a fusiform aneurysm, and gave high fuzzy degrees and high priorities for the detected aneurysms.

We have developed a diagnostic Case-Based Reasoning (CBR) system, Doctor, which infers possible defects in a home electrical appliance and lists up necessary service parts. The CBR is suitable to build a diagnostic system for the field service because the CBR imitates how experienced service technicians infer and is able to learn defect trends and novel repair cases from a service report database. In order to apply a CBR system to this real-world problem, Our system has the following new features: (1) Its CBR mechanism utilizes not only repair cases, but also diagnostic rules that are elicited from human experts so that accurate diagnosis can be achieved. (2) Its casebase maintenance mechanism updates the casebase and adapts it to the changing real world.

In the domains involving environmental changes, some knowledge and heuristics which were useful for solving problems in the previous environment often become unsuitable for problems in the new environment. This paper describes two approaches to solve such problems in the context of case-based reasoning systems. The first one is maintaining descriptions of applicable scopes of cases through generalization and specialization. The generalization is performed to expand problem descriptions, i. e. descriptions of applicable scopes of cases. On the other hand, the specialization is performed to narrow problem descriptions of cases which failed to be applied to given problems with the aim of dealing with environmental changes. The second approach is forgetting, that is deleting obsolete cases from the case-base. However, the domain-dependent knowledge is necessary for testing obsolescence of cases and that causes the problem of knowledge acquisition. We adopt the strategies used by conventional learning systems and extend them using the least domain-dependent knowledge. These two approaches for adapting the case-base to the environment are evaluated through simulations in the domain of electric power systems.

For the improvement of software quality and productivity, the author aims at realizing a software development environment to develop software through utilizing the merits of group work. Since networking is necessary for collaborative software development, he has developed a software distributed development environment for collaborative software development. In this environment, discussions about software design are held through a communication network, and the contents of discussions are recorded as software design decisions and decision rationale. One feature of this environment is that the contents of discussions can be recorded in on-line real time and reused without reconstructing the information recorded through this environment. This paper clarifies the essential conditions for actualizing this environment and proposes an information structure model for recording the contents of discussions that actualizes the above-mentioned feature. The effectiveness of the proposed model is proved through an example of its application to software design discussions.

In the information engineering learning environment, there may be more than one solution to any given problem. We have developed CAMELOT using the Nominal Group Technique for group problem solving. This paper describes the collaborative learning system on the Internet using discussion model, the effectiveness of collaborative learning in modeling the entity-relationship diagram within the field of information engineering, and how to apply AI technologies such as rule-based reasoning and case-based reasoning to the pedagogical strategy. By using CAMELOT, each learner learns how to analyze through case studies and how to collaborate with his or her group in problem solving. As a result. We have found evidence for the effectiveness of collaborative learning, such as getting a deeper understanding by using CAMELOT than by individual learning, because they can reach better solutions through discussion, tips from other learners, examination of one another's individual solutions, and understanding alternative solutions using case-based reasoning.

We present basic strategies for memory-restricted forgetting mechanisms of cases and propose a forgetting strategy which is a combination of the basic strategies. The effectivness of the proposed strategy for improving the performance of case-based reasoning systems is demonstrated through simulations in the electric power systems.