Wooju Kim

Combination of multiple classifiers for the customer's purchase behavior prediction

In these days, EC companies are eager to learn about their customers using data mining technologies. But the diverse situations of such companies make it difficult to know which is the most effective algorithm for the given problems. Recently, a movement towards combining multiple classifiers has emerged to improve classification results. In this paper, we propose a method for the prediction of the EC customers purchase behavior by combining multiple classifiers based on genetic algorithm. The method was tested and evaluated using Web data from a leading EC company. We also tested the validity of our approach in general classification problems using handwritten numerals. In both cases, our method shows better performance than individual classifiers and other known combining methods we tried.

DAS : intelligent scheduling systems for shipbuilding

Daewoo Shipbuilding Company, one of the largest shipbuilders in the world, has experienced great deal of trouble with the planning and scheduling of its production process. To solve the problems, from 1991 to 1993, Korea Advanced Institute of Science and Technology (KAIST) and Daewoo jointly conducted the Daewoo Shipbuilding Scheduling (das) Project. To integrate the scheduling expert systems for shipbuilding, we used a hierarchical scheduling architecture. To automate the dynamic spatial layout of objects in various areas of the shipyard, we developed spatial scheduling expert systems. For reliable estimation of person-hour requirements, we implemented the neural network-based person-hour estimator. In addition, we developed the paneled-block assembly shop scheduler and the long-range production planner. For this large-scale project, we devised a phased development strategy consisting of three phases: (1) vision revelation, (2) data-dependent realization, and (3) prospective enhancement. The DAS systems were successfully launched in January 1994 and are actively being used as indispensable systems in the shipyard, resulting in significant improvement in productivity and visible and positive effects in many areas.

UNIK-OPT/NN: neural network based adaptive optimal controller on optimization models

When the future information for an optimization model is not complete, the model tends to incorporate such uncertainties as some assumptions on the coefficients. As time passes and more precise information is accumulated, the initial optimal solution may no longer be optimal, or even feasible. At this point, model builders want to modify the assumed and controllable coefficients to obtain the desired values of designated decision variables. To aid this process, a neural network could effectively be applied. So we develop a tool UNIK-OPT/NN which can support the construction and recall of the neural network model on top of the knowledge assisted optimization model formulator UNIK-OPT and the semantic neural network building aid UNIK-NEURO. By adopting a commonly interpretable semantic representation of optimization and neural network models, UNIK-OPT/NN can effectively automate most of the neural network construction and recall procedure for optimal control.

Prevalence of and risk factors for extracranial internal carotid artery stenosis in Korean Type 2 diabetic patients

Aims  The objectives of this study were to evaluate the prevalence of and risk factors for extracranial internal carotid artery stenosis in Type 2 diabetic patients.

A knowledge-based maintenance of legacy systems: METASOFT

Abstract The maintenance of legacy systems is a continuous problem in the field of software maintenance. To assist in the maintenance of legacy systems, we have represented the legacy systems and the maintenance requirement in a compatible manner so that the maintenance requirement can be a clue for identifying the relevant program clauses and data items in the database. For this purpose, a maintenance component is represented by the maintenance mode (add, modify or delete) and property and key words. The corresponding information about the programs clauses is extracted from the source code of the legacy program by reverse engineering. The maintenance point identification algorithm—MPI algorithm—proposed in this research is theoretically complete and relatively efficient, and is proved so empirically. Using this approach, the system METASOFT has been developed for the Korea Electric Power Corporation which uses the COBOL programs and IMS database. It turns out that the system is well accepted by the users.