• Asian-Australasian Journal of Animal Sciences
• /
• v.15 no.4
• /
• pp.591-595
• /
• 2002

Three hundred thirty five progeny testing steers of Korean beef cattle were evaluated ultrasonically for back fat thickness (BFT), longissimus muscle area (LMA) and intramuscular fat (IF) before slaughter. Class measurements associated with the Korean yield grade and quality grade were also obtained. Residual standard deviation between ultrasonic estimates and carcass measurements of BFT, LMA were 1.49 mm and $0.96cm^2$. The linear correlation coefficients (p<0.01) between ultrasonic estimates and carcass measurements of BFT, LMA and IF were 0.75, 0.57 and 0.67, respectively. Results for improving predictions of yield grade by four methods-the Korean yield grade index equation, fat depth alone, regression and decision tree methods were 75.4%, 79.6%, 64.3% and 81.4%, respectively. We conclude that the decision tree method can easily predict yield grade and is also useful for increasing prediction accuracy rate.

• 한국데이터정보과학회:학술대회논문집
• /
• 2004.04a
• /
• pp.39-50
• /
• 2004

The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud dection, data reduction and variable screening, category merging, etc. CHAID(Chi-square Automatic Interaction Detector) uses the chi-squired statistic to determine splitting and is an exploratory method used to study the relationship between a dependent variable and a series of predictor variables. In this paper we propose CHAID algorithm by cube-based proportional sampling and explore CHAID algorithm in view of accuracy and speed by the number of variables.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.4
• /
• pp.803-816
• /
• 2004

The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud dection, data reduction and variable screening, category merging, etc. CHAID uses the chi-squired statistic to determine splitting and is an exploratory method used to study the relationship between a dependent variable and a series of predictor variables. In this paper we propose CHAID algorithm by cube-based proportional sampling and explore CHAID algorithm in view of accuracy and speed by the number of variables.

• Child Health Nursing Research
• /
• v.16 no.1
• /
• pp.84-92
• /
• 2010

Purpose: One of the main reasons why mothers quit breast feeding is that the volume of breast milk is inadequate due to insufficiency in suckling. We believe suckling experience may be a factor affecting nipple confusion. So an alternative feeding method, namely cup, spoon, finger, or nasogastric tube feeding may be needed to prevent nipple confusion. The purpose of this study was to construct a predictive model for demand for alternative feeding education by nurses. Methods: A descriptive design with structured self-report questionnaires was used for this study. Data from 175 nurses working in hospitals in Busan were collected between April 1 and 15, 2009. Data were analyzed by decision tree method, one of the data mining techniques using SAS 9.1 and Enterprise Miner 4.3 program. Results: Of the nurses, 81.1% demanded alternative feeding education and 5 factors showed that most of them expressed intention to pay, desire to know about alternative feeding, age, and learning experience. From these results, the derived model is considered appropriative for explaining and predicting demand for alternative feeding education. Conclusion: This confirms that knowledge and compliance in alternative breast feeding for newborn babies should be correct and any inaccuracies or insufficient information should be supplemented.

• Journal of Engineering Education Research
• /
• v.21 no.4
• /
• pp.46-52
• /
• 2018

In general, lecture evaluation has been used in most universities as an important criterion to evaluate quality of education. This study is exploratory research on the predictors that determine lecture evaluation in college of engineering to give practical implications for improvement of engineering education. For the exploration of predictors of lecture evaluation, the data of lecture evaluation in A College of Engineering located in the metropolitan area was used, and Decision Tree Analysis was utilized as an analysis method. As a result, the characteristics of students turned out to be the most distinct predictor comparing with those of course and instructor at lecture evaluation in college of engineering. That is, as various elements other than teaching competency influence lecture evaluation in college of engineering, it is necessary to be more careful in evaluating quality of lecture or teaching competence. Thus, a follow-up study should be conducted to adjust the influence by the predictors that instructors can hardly control.

• ETRI Journal
• /
• v.44 no.3
• /
• pp.462-475
• /
• 2022

Although researchers have proposed numerous techniques for speech emotion recognition, its performance remains unsatisfactory in many application scenarios. In this study, we propose a speech emotion recognition model based on a genetic algorithm (GA)-decision tree (DT) fusion of deep and acoustic features. To more comprehensively express speech emotional information, first, frame-level deep and acoustic features are extracted from a speech signal. Next, five kinds of statistic variables of these features are calculated to obtain utterance-level features. The Fisher feature selection criterion is employed to select high-performance features, removing redundant information. In the feature fusion stage, the GA is is used to adaptively search for the best feature fusion weight. Finally, using the fused feature, the proposed speech emotion recognition model based on a DT support vector machine model is realized. Experimental results on the Berlin speech emotion database and the Chinese emotion speech database indicate that the proposed model outperforms an average weight fusion method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.492-494
• /
• 2021

The decision tree technique is used as a classification technique in machine learning. However, the decision tree has a problem of consuming a lot of speed or resources due to the problem of overfitting. To solve this problem, there are bagging and boosting techniques. Bagging creates multiple samplings and models them using them, and boosting models the sampled data and adjusts weights to reduce overfitting. In addition, recently, techniques Xgboost have been introduced to improve performance. Therefore, in this paper, we collect wifi signal data for indoor positioning, apply it to the existing method and Xgboost, and perform performance evaluation through it.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.1B
• /
• pp.33-40
• /
• 2008

Intensive rainfalls in wet season (June~September) result in serious flood damage which is about 95% of natural hazard in Korea. Recently, in order to cope with repeated flood hazard, comprehensive flood control plans have been carried out in large basins in Korea. The plans suggest structural alternative plans for flood mitigation as well as non-structural plans. In this study, a practical method using a decision tree was developed to systematically allocate structural facilities for flood control, which maximizes the flood control capacity in a basin. This study also presents a practical guidance to organize structural defensive alternatives for a comprehensive flood control plan in a large basin.

• Asia pacific journal of information systems
• /
• v.6 no.2
• /
• pp.77-105
• /
• 1996

The accuracy of knowledge is a major concern for expert system developers and users. Machine learning approaches have recently been found to be useful in knowledge acquisition for expert systems. However, the accuracy of concept acquired from machine learning could not be analyzed in most cases. In this paper we develop a comprehensive knowledge error analysis methodology for practical use of expert systems. Decision tree induction is an important type of machine learning method for business expert systems. Here we start to analyze with knowledge acquired from decision tree induction method, and extend the results to develop error analysis methodology for general machine learning methods. We give several examples and illustrations for these results. We also discuss the applicability of these results to multistrategy learning approaches.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2006.05a
• /
• pp.554-557
• /
• 2006

For various historical and technical reasons, the safety of dams has been controlled by an engineering standards-based approach, which has been developed over many years, initially for the design of new dams, but increasingly applied over the past few decades to assess the safety of existing dams. And some countries were asked for risk assessment on existing dam, which included structural, hydraulic safety of dam and social risk. Whereas other countries have developed and adapted as an additional tool to assist in decision-making for dam safety management. Dam risk analysis should need the reliability data of dam failures, the past constructed history and management records of existing dam. It is thought with risk analysis method of dams for structural safety management in domestic that suitable to use consider an event tree, fault tree and conditioning indexes method.