• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.144-154
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• 2003

In this paper we present Logical Evolution method which is a new teaming algorithm for the concepts expressed as binary logic function. We try to solve some problems of Inductive Learning algorithms through Logical Evolution. First, to be less affected from limited prior knowledge, it generates features using the gained informations during learning process and learns the concepts with these features. Second, the teaming is done using not the whole example set but the individual example, so even if new problem or new input-output variables are given, it can use the previously generated features. In some cases these old features can make the teaming process more efficient. Logical Evolution method consists of 5 operations which are selected and performed by the logical evaluation procedure for feature generation and learning process. To evaluate the performance of the present algorithm, we make experiments on MONK data set and a newly defined problem.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.614-621
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• 2006

We have implemented a multiclassifier learning approach in a GA-based inductive learning environment that learns classification rules that are similar to rules used in PROSPECTOR. In the multiclassifier learning approach, a classification system is constructed with several classifiers that are obtained by running a GA-based learning system several times to improve the overall performance of a classification system. To implement the multiclassifier learning approach, we need a decision-making scheme that can draw a decision using multiple classifiers. In this paper, we introduce two decision-making schemes: one is based on combining posterior odds given by classifiers to each class and the other one is a voting scheme based on ranking assigned to each class by classifiers. We also present empirical results that evaluate the effect of the multiclassifier learning approach on the GA-based inductive teaming environment.

• Journal of Korean Elementary Science Education
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• v.26 no.2
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• pp.201-208
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• 2007

The purpose of this study was to analyze the structure and type of prospective elementary school teachers' scientific hypotheses generated on zoological tasks. The subjects were 18 prospective elementary school teachers. Four zoological hypothesis generation tasks were developed and administered to the subjects. After being presented with the zoological situations of the tasks, the subjects were asked to generate causal questions and scientific hypotheses. The scientific hypotheses were analyzed by the inductive approach. The results of this study showed that the hypotheses contained explicans and explicanda. The explicans were divided into two parts: 'what' and 'how'. In some cases, additional explanations were attached to the 'what' section. In addition, the hypotheses were classified into 9 types. The number of explicanda, the pattern of explicans, and the number of explicans were used as criteria for classification purposes. This study also discussed the implications of these findings for future directions in teaching and teaming in science education.

• The Journal of Information Systems
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• v.15 no.4
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• pp.211-224
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• 2006

This study investigates the application of data mining techniques such as artificial neural networks, rough sets, and induction teaming to the intrusion detection systems. To maximize the effectiveness of data mining for intrusion detection systems, we introduced the asymmetric costs with false positive errors and false negative errors. And we present a method for intrusion detection systems to utilize the asymmetric costs of errors in data mining. The results of our empirical experiment show our intrusion detection model provides high accuracy in intrusion detection. In addition the approach using the asymmetric costs of errors in rough sets and neural networks is effective according to the change of threshold value. We found the threshold has most important role of intrusion detection model for decreasing the costs, which result from false negative errors.

• School Mathematics
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• v.10 no.1
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• pp.1-22
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• 2008

The purpose of this study is to search the effective teaching-learning program by considering how affect on formation of linear function using Excel. This study was based on qualitative case study. The teaching experiment using Excel executed with five 8th graders' students for second research content. Teaching experiment was performed for two classes. Collecting the data was conducted via observations and interviews with students. The data include audio and video recording of the students' work, students' worksheets and detailed field notes. The conclusions drawn from teaching experiment are as follows: First, when students explored relevancy content of function in Excel environment, formation of concept of function was facilitated by experiencing operation of algebraic formulas, tables and graphs. We could infer that formation of concept was effected by conjecture activity and iterative process of feedback through Excel environment. Second, the students explored the changes very interestingly making algebraic formulas and presenting tables and graphs. The students were familiarized with observation on algebraic formulas, graphs and tables concurrently. Also, they tried to look for general rules through inductive observation. According to this study, we noticed that exploration teaming environment using Excel could supplement paper-and-pencil environment.