• Journal of Korean Institute of Industrial Engineers
• /
• v.33 no.2
• /
• pp.183-190
• /
• 2007

We present a new decision tree classification algorithm using rough set theory that can induce classification rules, the construction of which is based on core attributes and relationship between objects. Although decision trees have been widely used in machine learning and artificial intelligence, little research has focused on improving classification quality. We propose a new decision tree construction algorithm that can be simplified and provides an improved classification quality. We also compare the new algorithm with the ID3 algorithm in terms of the number of rules.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.29 no.2
• /
• pp.13-19
• /
• 2006

The instance-based learning is a machine learning technique that has proven to be successful over a wide range of classification problems. Despite its high classification accuracy, however, it has a relatively high storage requirement and because it must search through all instances to classify unseen cases, it is slow to perform classification. In this paper, we have presented a new data reduction method for instance-based learning that integrates the strength of instance partitioning and attribute selection. Experimental results show that reducing the amount of data for instance-based learning reduces data storage requirements, lowers computational costs, minimizes noise, and can facilitates a more rapid search.

• Journal of Korean Institute of Industrial Engineers
• /
• v.40 no.4
• /
• pp.375-381
• /
• 2014

Data mining is a process of discovering useful patterns or information from large amount of data. Decision tree is one of the data mining algorithms that can be used for both classification and prediction and has been widely used for various applications because of its flexibility and interpretability. Decision trees for classification generally generate a number of rules that belong to one of the predefined category and some rules may belong to the same category. In this case, it is necessary to determine the significance of each rule so as to provide the priority of the rule with users. The purpose of this paper is to propose a rule selection method in classification tree models that accommodate the umber of observation, accuracy, and effectiveness in each rule. Our experiments demonstrate that the proposed method produce better performance compared to other existing rule selection methods.

• Industrial Engineering and Management Systems
• /
• v.13 no.4
• /
• pp.449-453
• /
• 2014

Chronic diseases including hypertension and its complications are major sources causing the national medical expenditures to increase. We aim to predict the risk of hypertension complications for hypertension patients, using the sample national healthcare database established by Korean National Health Insurance Corporation. We apply classification techniques, such as logistic regression, linear discriminant analysis, and classification and regression tree to predict the hypertension complication onset event for each patient. The performance of these three methods is compared in terms of accuracy, sensitivity and specificity. The result shows that these methods seem to perform similarly although the logistic regression performs marginally better than the others.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.9 no.6
• /
• pp.1889-1893
• /
• 2008

This paper explores the classification performance of applying to support vector machines (SVMs) for the image classification problems. In this study, we extract the color, texture and shape features of natural images and compare the performance of image classification using each individual feature and integrated features. The experiment results show that classification accuracy on the basis of color feature is better than that based on texture and shape features and the results of the integrating features also provides a better and more robust performance than individual feature. In additions, we show that the proposed classifier of SVM based approach outperforms BPNN to corporate the image classification problems.

• Journal of Korean Institute of Industrial Engineers
• /
• v.28 no.2
• /
• pp.223-231
• /
• 2002

Recently, work-related musculoskeletal disorders(WMSDs) have rapidly increased and have been a major issue in the field of industrial safety. Of several physical risk factors for WMSDs, which include postures, vibration, repetitive work, speed or acceleration of movements, etc., awkward postures have been known as one of the major causes of WMSDs. For reducing the potential for injury as a result of postures, cost effective quantification of the magnitude for physical exposure to poor working postures is important and needed. To do this, several postural classification schemes have been developed and used in industrial sites. It is known that perceived discomfort for joint motions and muscle strength for females were much less than those for males. However, the existing postural classification schemes were developed without considering these gender effects. This study aims to develop a new postural classification scheme for female workers, based on the perceived discomfort for joint motions. The result showed that there was significant difference between the schemes for female and male. It was also found that when compared with OWAS, RULA and REBA, postural load was quantified more precisely with the developed scheme. It is recommended that different schemes according to gender of workers involved in work be used in order to accurately evaluate postural load of work postures.

• Archives of design research
• /
• v.17 no.3
• /
• pp.71-80
• /
• 2004

The culture industrial design classification is the major factor to develop the various potential abilities, promote a new value to change and maximize the competitiveness of industry and the nation. Accordingly it is important to comprehensively understand and recognize the changing of this new design paradigm with its structural contents. In order to have universality data need to be made a classification system in advance for the policy, strategy, analysis indicator and assessing the culture industrial design classification. This study build CIDC that structure divided into three sections(A,B,C) with divisions(medium), classes(small) and groups(specific field) of numeric code from "01" or "001" and increasing their numbers. CIDC is set up in order to search for differences and referential character through comparisons with design-related classification systems as well as making a role of the basic under data for this field.c under data for this field.

• Journal of Korean Institute of Industrial Engineers
• /
• v.36 no.1
• /
• pp.13-21
• /
• 2010

The prediction of contract cancellation of customers is essential in insurance companies but it is a difficult problem because the customer database is large and the target or cancelled customers are a small proportion of the database. This paper proposes a new data mining approach to the binary classification by handling a large-scale unbalanced data. Over-sampling, clustering, regularized logistic regression and boosting are also incorporated in the proposed approach. The proposed approach was applied to a real data set in the area of insurance and the results were compared with some other classification techniques.

• Industrial Engineering and Management Systems
• /
• v.2 no.1
• /
• pp.35-44
• /
• 2003

Recovery of end-of-life (EOL) products is an environmentally and economically sound way to achieve many of the goals of sustainable development. Many product recovery systems are dependent upon destructive disassembly such as shredding, which undesirably causes a large volume of shredder dust and makes parts reuse impossible. Although non-destructive disassembly has been considered as an alternative for solving the problems, the classification of disassembled items has not been sufficiently investigated. In this paper, we propose a model that mathematically optimizes the disassembly and classification of EOL products. Based on the AND/OR graph that illustrates all possible disassembly sequences of a given product, we identify the physical properties that are considered as constraints in the model. As a result of the solution procedure, the recovery problem can be transformed into a mixed integer linear programming (MILP) model. We show an example that illustrates the concept of our model.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.5
• /
• pp.931-940
• /
• 2011

Classification is to generate a rule of classifying objects into several categories based on the learning sample. Good classification model should classify new objects with low misclassification error. Many types of classification methods have been developed including logistic regression, discriminant analysis and tree. This paper presents a new classification method using penalized partial least squares. Penalized partial least squares can make the model more robust and remedy multicollinearity problem. This paper compares the proposed method with logistic regression and PCA based discriminant analysis by some real and artificial data. It is concluded that the new method has better power as compared with other methods.