• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권5호
• /
• pp.2523-2538
• /
• 2017

In most of the existing literature, the definition of the class label has the following characteristics. First, the class label of the samples from the same object has an absolutely fixed value. Second, the difference between class labels of the samples from different objects should be maximized. However, the appearance of a face varies greatly due to the variations of the illumination, pose, and expression. Therefore, the previous definition of class label is not quite reasonable. Inspired by discriminative least squares regression algorithm (DLSR), a noisy label based discriminative least squares regression algorithm (NLDLSR) is presented in this paper. In our algorithm, the maximization difference between the class labels of the samples from different objects should be satisfied. Meanwhile, the class label of the different samples from the same object is allowed to have small difference, which is consistent with the fact that the different samples from the same object have some differences. In addition, the proposed NLDLSR is expanded to the kernel space, and we further propose a novel kernel noisy label based discriminative least squares regression algorithm (KNLDLSR). A large number of experiments show that our proposed algorithms can achieve very good performance.

• International Journal of Computer Science & Network Security
• /
• 제23권9호
• /
• pp.129-133
• /
• 2023

Classification or prediction problem is how to solve it using a specific feature to obtain the predicted class. A wheat seeds specifications 4 3 classes of seeds will be used in a prediction process. A multi linear regression will be built, and a prediction error ratio will be calculated. To enhance the prediction ratio an ANN model will be built and trained. The obtained results will be examined to show how to make a prediction tool capable to compute a predicted class number very close to the target class number.

• 한국환경보건학회지
• /
• 제37권6호
• /
• pp.460-466
• /
• 2011

Objectives: Students in elementary schools usually wash their hands in a washstand. However, little attention is paid to the washstand itself. Today, the importance of personal sanitation and hygiene is greatly emphasized. Therefore students' parents and the public are growing increasingly interested in accessibility to washstands by elementary school students in their schools. Methods: With respect to this study, a survey of students and teachers inelementary schools was performed on the installation of washstands in order to determine the proper number of washstands per school. Results: The results show that 1.1 boys (per class) need a washstand, while 1.8 girls (per class) do so in order to maintain a 50% level of crowdedness. By of the regression equation, to maintain 50% congestion (50% of all students feel congestion) there should be 18.5 boys, and the 15.76 girls per washstand. Table 3 is based on the above results, the number of students per washstand (x) and congestion (y), separated by gender according to the results of regression analysis, the correlation of male models in the linear regression analysis and correlation of girls in the regression equation can be obtained. The linear regression fit of less than 0.7 determines that the coefficients of determination are 0.5399 and 0.4195, respectively. Significance was much smaller. Also, according to the simulation using the diffusion model, with 29 students per class more than one washstand should be provided in a school. Girls (per class) need 0.7 more washstands than boys (per class). Conclusions: More washstand facilities for girls than boys are needed. If the target is based on school class size two washstands should be installed. Finally, guidelines and/or standards in the Schools Health Act of Korea forin elementary school washstands is considerably needed.

• 한국인공지능학회지
• /
• 제9권1호
• /
• pp.9-14
• /
• 2021

This paper predicted a model that indicates whether to buy a car based on primary health insurance customer data. Currently, automobiles are being used to land transportation and living, and the scope of use and equipment is expanding. This rapid increase in automobiles has caused automobile insurance to emerge as an essential business target for insurance companies. Therefore, if the car insurance sales are predicted and sold using the information of existing health insurance customers, it can generate continuous profits in the insurance company's operating performance. Therefore, this paper aims to analyze existing customer characteristics and implement a predictive model to activate advertisements for customers interested in such auto insurance. The goal of this study is to maximize the profits of insurance companies by devising communication strategies that can optimize business models and profits for customers. This study was conducted through the Microsoft Azure program, and an automobile insurance purchase prediction model was implemented using Health Insurance Cross-sell Prediction data. The program algorithm uses Two-Class Logistic Regression and Two-Class Boosted Decision Tree at the same time to compare two models and predict and compare the results. According to the results of this study, when the Threshold is 0.3, the AUC is 0.837, and the accuracy is 0.833, which has high accuracy. Therefore, the result was that customers with health insurance could induce a positive reaction to auto insurance purchases.

• 한국해양공학회지
• /
• 제23권6호
• /
• pp.71-76
• /
• 2009

This paper describes a statistical analysis method for predicting a coast fishing boat's effective horsepower. The EHP estimation method for small coast fishing boats was developed, based on a statistical regression analysis of model test results in a circulating water channel. The statistical regression formula of a fishing boat's effective horsepower is determined from the regression analysis of the resistance test results for 15 actual coast fishing boats. This method was applied to the effective horsepower prediction of a G/T 4 ton class coast fishing boat. From the estimation of the effective horsepower using this regression formula and the experimental model test of the G/T 4 ton class coast fishing boat, the estimation accuracy was verified under 10 percent of the design speed. However, the effective horsepower prediction method for coast fishing boats using the regression formula will be used at the initial design and hull-form development stage.

• 한국수학교육학회지시리즈E:수학교육논문집
• /
• 제34권4호
• /
• pp.485-506
• /
• 2020

본 연구는 교사의 수학적 신념이 학생의 수학적 신념에 주는 영향을 잠재집단회귀모델(Latent Class Regression Model; LCRM)을 통해 분석하였다. 분석을 위해 본 연구는 잠재집단분석(Latent Class Analysis; LCA)을 통해 교사 60명과 그 교사에게 배우는 학생 1850명의 수학적 신념을 각각 분류한 강성권, 홍진곤(2020)의 연구결과를 활용하였다. 분석결과, '수학의 본질'에 대한 교사의 신념은 학생의 '수학교과', '수학문제해결', '수학학습' 신념에 영향을 주었다. 또한, '수학의 교수'와 '수학적 능력'에 관한 교사의 신념은 학생의 '수학교과', '수학문제해결', '자아개념' 신념에 영향을 주었다. 이를 통해 본 연구는 교사의 수학적 신념이 학생의 수학적 신념에 실질적인 영향을 끼친다는 것을 통계적으로 실증하였다. 이러한 연구결과는 교사들의 연수와 관련한 목표와 내용의 설정에 도움을 줄 수 있을 것이다.

• Journal of Korea Artificial Intelligence Association
• /
• 제1권1호
• /
• pp.1-6
• /
• 2023

This study developed models using decision forest, support vector machine, and logistic regression methods to predict and prevent suicidal ideation among Korean adolescents. The study sample consisted of 51,407 individuals after removing missing data from the raw data of the 18th (2022) Youth Health Behavior Survey conducted by the Korea Centers for Disease Control and Prevention. Analysis was performed using the MS Azure program with Two-Class Decision Forest, Two-Class Support Vector Machine, and Two-Class Logistic Regression. The results of the study showed that the decision forest model achieved an accuracy of 84.8% and an F1-score of 36.7%. The support vector machine model achieved an accuracy of 86.3% and an F1-score of 24.5%. The logistic regression model achieved an accuracy of 87.2% and an F1-score of 40.1%. Applying the logistic regression model with SMOTE to address data imbalance resulted in an accuracy of 81.7% and an F1-score of 57.7%. Although the accuracy slightly decreased, the recall, precision, and F1-score improved, demonstrating excellent performance. These findings have significant implications for the development of prediction models for suicidal ideation among Korean adolescents and can contribute to the prevention and improvement of youth suicide.

• Journal of the Korean Statistical Society
• /
• 제29권4호
• /
• pp.407-422
• /
• 2000

This article is concerned with the selecting predictor variables to be included in building a class of binary response t-link regression models where both probit and logistic regression models can e approximately taken as members of the class. It is based on a modification of the stochastic search variable selection method(SSVS), intended to propose and develop a Bayesian procedure that used probabilistic considerations for selecting promising subsets of predictor variables. The procedure reformulates the binary response t-link regression setup in a hierarchical truncated normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. In this setup, the most promising subset of predictors can be identified as that with highest posterior probability in the marginal posterior distribution of the hyperparameters. To highlight the merit of the procedure, an illustrative numerical example is given.

• Computers and Concrete
• /
• 제8권5호
• /
• pp.583-595
• /
• 2011

This paper studied the cement efficiency factor (k factor) of high calcium Class F fly ash. This k factor represents a unit of fly ash with efficiency equivalent to k unit of cement. The high calcium Class F fly ash was used to replace cement in concrete. The modified Bolomey's law with linear relationship was used for the analysis of the result of compressive strength, cement to water ratio (c/w) and fly ash to water ratio (f/w) by using the multi-linear regression to determine the k factor and other constants in the equations. The results of analysis were compared with the results from other researcher and showed that the k factor of high calcium Class F fly ash depends on the fineness of fly ash, replacement level and curing age. While the amount of CaO content in Class F fly ash not evident. Furthermore, necessary criteria and variables for the determination of the k factor including the use of the k factor in concrete mix design containing fly ash were proposed.

• Restorative Dentistry and Endodontics
• /
• 제38권2호
• /
• pp.79-84
• /
• 2013

Objectives: This study aimed to investigate the effect of clinical clerkship-associated achievements, such as performance of procedures at the student clinic, observation, and attitude towards a clerkship, on the objective structured clinical examination (OSCE) scores of dental students graduating in restorative dentistry. Materials and Methods: The OSCEs consisted of two stations designed to assess students' clinical skills regarding cavity preparation for a class II gold inlay and a class IV composite restoration. The clerkship achievements, consisting of the number of student clinical procedures performed, observation-related OSCE, and scores of their attitudes towards a conservative dentistry clerkship, were assessed. Correlation and multiple regression analyses were conducted. Results: The correlation coefficient between the OSCE scores for cavity preparation for a class II gold restoration and clerkship attitude scores was 0.241 (p < 0.05). Regarding a class IV composite restoration, OSCE scores showed statistically significant correlations with the observation (r = 0.344, p < 0.01) and attitude (r = 0.303, p < 0.01) scores. In a multiple regression analysis, attitudes towards a clerkship (p = 0.033) was associated with the cavity preparation for a class II gold inlay OSCE scores, while the number of procedure observations (p = 0.002) was associated with the class IV composite restoration OSCE scores. Conclusions: The number of clinical procedures performed by students, which is an important requirement for graduation, showed no correlation with either of the OSCEs scores.