• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1424-1434
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• 2009

DNA computing-inspired pattern classification based on the hypernetwork model is a novel approach to pattern classification problems. The hypernetwork model has been shown to be a powerful tool for multi-class data analysis. However, the ordinary hypernetwork model has limitations, such as operating sequentially only. In this paper, we propose a efficient implementing method of DNA computing-inspired pattern classifier using GPU. We show simulation results of multi-class pattern classification from hand-written digit data, DNA microarray data and 8 category scene data for performance evaluation. and we also compare of operation time of the proposed DNA computing-inspired pattern classifier on each operating environments such as CPU and GPU. Experiment results show competitive diagnosis results over other conventional machine learning algorithms. We could confirm the proposed DNA computing-inspired pattern classifier, designed on GPU using CUDA platform, which is suitable for multi-class data classification. And its operating speed is fast enough to comply point-of-care diagnostic purpose and real-time scene categorization and hand-written digit data classification.

• Journal of Korean Society for Quality Management
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• v.31 no.2
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• pp.17-39
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• 2003

The 21st century is called “an era of creation” or “an era of speed.” These are flat expressions requiring a fierce competition between individuals, corporations and nations. In a reality in which we should make new things continuously within a short period of time, the world best benchmarking can become a good alternative. The world best practice can be called “a mode of operation” that has created the world's best performance in a particular field of managerial activities. It is very meaningful for the nations' corporations, which have a lower competitiveness than world-class ones and weak points in the area of human resources development, in particular, to benchmark the world-class corporations' best practices of HRD. Therefore, this study is conducted in conformity with a model of the Instructional Systems Designs for the Total Quality Education that brings the structure of the world-class corporations' best practices of HRD into line with that of the Total Quality Management. That is, analysis, design, development, implementation and assessment are included in this study.

• Journal of the Korean Society of Safety
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• v.13 no.4
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• pp.230-239
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• 1998

Though limited, Model Human Processor (MHP) has been used to explain the complex users' behaviors during human-computer interactions in a simplified manner. MHP consists of perceptual, cognitive and motor systems, each with processors and memories interacting with each other in serial or parallel mode. The important parameters of memory include the storage capacity, the decay time, and the code type of a memorized item. The important parameter of a processor is the cycle time. Using these features of the model, this study developed a computerized cognitive process simulator to predict the cognitive process time of a class match task process. An experimental validity test result showed that the mean prediction time for cognitive process of the class match task simulated 50 times by the simulator was consistent with the mean cognitive process time of the same task performed by 37 subjects. Animation of the data flow during the class match task simulation will help understand the invisible human cognitive process.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.301-314
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• 2012

In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on the Multi-Class Support Vector Machine (MSVM) and the Hidden Markov Model (HMM) classifiers are presented. A feature vector is extracted from the text pair that is under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the Multi-Class Support Vector Machine and Hidden Markov Model. Another set of data was used for testing. The results of the MSVM and HMM outperform the results of the length based approach. Moreover these new approaches are valid for any language pairs and are quite flexible since the feature vector may contain less, more, or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research.

• Journal of the Society of Naval Architects of Korea
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• v.45 no.3
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• pp.247-257
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• 2008

In this paper, a free running system, which is developed recently for a 2m-class ship models, will be introduced. For the remote control of hardware, GUI of software packages was developed using Visual Basic 6.0, and Host PC with Positioning Board manages Servo drive. Then the drive operates propeller and rudder. Its control performance will be shown. Also its adaptability to the resistance, manoeuverability and seakeeping model tests will be considered through the installation on a KTTC standard ship model from MOERI.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.145-152
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• 2015

We consider an online selective-sample learning problem for sequence classification, where the goal is to learn a predictive model using a stream of data samples whose class labels can be selectively queried by the algorithm. Given that there is a limit to the total number of queries permitted, the key issue is choosing the most informative and salient samples for their class labels to be queried. Recently, several aggressive selective-sample algorithms have been proposed under a linear model for static (non-sequential) binary classification. We extend the idea to hidden Markov models for multi-class sequence classification by introducing reasonable measures for the novelty and prediction confidence of the incoming sample with respect to the current model, on which the query decision is based. For several sequence classification datasets/tasks in online learning setups, we demonstrate the effectiveness of the proposed approach.

• Journal of Engineering Education Research
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• v.22 no.1
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• pp.22-28
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• 2019

The purpose of this study is to develop instructional design model of flipped learning suitable for engineering education field and to draw out effects and improvements by applying it to actual lessons for engineering college students. Literature review and case studies were conducted to achieve the purpose of the study. For a case study, flipped learning was applied to 'creative problem solving methodology' which is a liberal arts course of engineering college at D university in Gyeonggi-do. As a result of the literature review, the PARTNER model was applied and weekly instructional guide was presented by each stage. In addition, the results of analysis on the reflection journal showed that the students were more able to achieve the deepening learning stage through active participation in class than the existing class, and found that they had a more challenging plan after the class.

• Journal of Engineering Education Research
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• v.21 no.4
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• pp.28-34
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• 2018

Design class through team activities is increasing in engineering education. Team-based education has been known to improve students' creativity, problem solving ability, cooperative ability, self-directed learning ability, and communication ability. How to organize a team is an important issue that affects the performance of team activities as well as student satisfaction. However, previous studies have focused on the causal relationship between team formation and the team's performance. This paper deals with how to organize a balanced team in a real class. When the basic characteristic values of students are givens, the aim is to make the sum of the characteristic values as fair as possible for each team. We propose a mathematical team formation model and show how to apply it through case studies.

• Journal of Engineering Education Research
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• v.25 no.6
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• pp.69-80
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• 2022

In this study, the application of flipped learning methodology to thermodynamics in mechanical engineering was examined, and how university students view flipped learning and the effects of flipped learning were analyzed. To analyze the effects of flipped learning, pre-class survey, assessment on learning in pre-class, team activities during class, and post-class survey were conducted. The analysis was also conducted on 33 students who took the thermodynamics course in mechanical engineering, and the PARTNER flipped learning model was applied to the class. The results of this study are as follows; In the preliminary survey, the students expected that the flip-learning class with team activities and teaching between team members would be helpful in improving their learning. In addition, students recognized that cooperative learning through a team was helpful for learning. The case reflecting the result of pre-learning evaluation to the subject grades showed higher pre-learning evaluation results than the case not reflecting the result of the pre-learning evaluation to the subject grades, and it was found that the pre-learning evaluation was acting as a factor to promote learning in pre-class. In post-class survey, the satisfaction with the flipped learning class was high, indicating that the effectiveness of the flipped learning class applied to the thermodynamics class was excellent.