• 대한상한금궤의학회지
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• v.14 no.1
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• pp.41-50
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• 2022

Objective : The purpose of this study is to explore the most suitable machine learning model algorithm for Shanghanlun diagnostic system classification using natural language processing (NLP). Methods : A total of 201 data items were collected from 『Shanghanlun』 and 『Clinical Shanghanlun』, 'Taeyangbyeong-gyeolhyung' and 'Eumyangyeokchahunobokbyeong' were excluded to prevent oversampling or undersampling. Data were pretreated using a twitter Korean tokenizer and trained by logistic regression, ridge regression, lasso regression, naive bayes classifier, decision tree, and random forest algorithms. The accuracy of the models were compared. Results : As a result of machine learning, ridge regression and naive Bayes classifier showed an accuracy of 0.843, logistic regression and random forest showed an accuracy of 0.804, and decision tree showed an accuracy of 0.745, while lasso regression showed an accuracy of 0.608. Conclusions : Ridge regression and naive Bayes classifier are suitable NLP machine learning models for the Shanghanlun diagnostic system classification.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.12
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• pp.108-115
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• 1994

Most pattern classifiers have been designed based on the ML (Maximum Likelihood) training algorithm which is simple and relatively powerful. The ML training is an efficient algorithm to individually estimate the model parameters of each class under the assumption that all class models in a classifier are statistically independent. That assumption, however, is not valid in many real situations, which degrades the performance of the classifier. In this paper, we propose a minimum-error-rate training algorithm based on the MAP (Maximum a Posteriori) approach. The algorithm regards the normalized outputs of the classifier as estimates of the a posteriori probability, and tries to maximize those estimates. According to Bayes decision theory, the proposed algorithm satisfies the condition of minimum-error-rate classificatin. We apply this algorithm to NPM (Neural Prediction Model) for speech recognition, and derive new disrminative training algorithms. Experimental results on ten Korean digits recognition have shown the reduction of 37.5% of the number of recognition errors.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.196-200
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• 2024

Weather forecasting is considered to be of utmost important among various important sectors such as flood management and hydro-electricity generation. Although there are various numerical methods for weather forecasting but majority of them are reported to be Mechanistic computationally demanding due to their complexities. Therefore, it is necessary to develop and build models for accurately predicting the weather conditions which are faster as well as efficient in comparison to the prevalent meteorological models. The study has been undertaken to forecast various atmospheric parameters in the city of Bangalore using Naïve Bayes algorithms. The individual parameters analyzed in the study consisted of wind speed (WS), wind direction (WD), relative humidity (RH), solar radiation (SR), black carbon (BC), radiative forcing (RF), air temperature (AT), bar pressure (BP), PM₁₀ and PM_2.5 of the Bangalore city collected from Air Quality Monitoring Station for a period of 5 years from January 2015 to May 2019. The study concluded that Naive Bayes is an easy and efficient classifier that is centered on Bayes theorem, is quite efficient in forecasting the various air pollution parameters of the city of Bangalore.

• Computers and Concrete
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• v.19 no.3
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• pp.275-282
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• 2017

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.80-88
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• 2024

Breast cancer remains a significant global health burden, necessitating accurate and timely detection for improved patient outcomes. Machine learning techniques have demonstrated remarkable potential in assisting breast cancer diagnosis by learning complex patterns from multi-modal patient data. This study comprehensively evaluates several popular machine learning models, including logistic regression, decision trees, random forests, support vector machines (SVMs), naive Bayes, k-nearest neighbors (KNN), XGBoost, and ensemble methods for breast cancer prediction using the Wisconsin Breast Cancer Dataset (WBCD). Through rigorous benchmarking across metrics like accuracy, precision, recall, F1-score, and area under the ROC curve (AUC), we identify the naive Bayes classifier as the top-performing model, achieving an accuracy of 0.974, F1-score of 0.979, and highest AUC of 0.988. Other strong performers include logistic regression, random forests, and XGBoost, with AUC values exceeding 0.95. Our findings showcase the significant potential of machine learning, particularly the robust naive Bayes algorithm, to provide highly accurate and reliable breast cancer screening from fine needle aspirate (FNA) samples, ultimately enabling earlier intervention and optimized treatment strategies.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.309-320
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• 2020

Bayesian networks, also known as directed acyclic graphs (DAG), are used in many areas of medicine, meteorology, and genetics because relationships between variables can be modeled with graphs and probabilities. In particular, Bayesian network classifiers, which are used to predict discrete data, have recently become a new method of data mining. Bayesian networks can be grouped into different models that depend on structured learning methods. In this study, Bayesian network models are learned with various properties of structure learning. The models are compared to the simplest method, the naïve Bayes model. Classification results are compared by applying learned models to various real data. This study also compares the relationships between variables in the data through graphs that appear in each model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.1-12
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• 2017

Traffic accidents are caused by a combination of human factors, vehicle factors, and environmental factors. In the case of traffic accidents where rental cars are involved, the possibility and the severity of traffic accidents are expected to be different from those of other traffic accidents due to the unfamiliar environment of the driver. In this study, we developed a model to forecast the severity of rental car accidents by using Naive Bayes classifier for Busan, Gangneung, and Jeju city. In addition, we compared the prediction accuracy performance of two models where one model uses the variables of which statistical significance were verified in a prior study and another model uses the entire available variables. As a result of the comparison, it is shown that the prediction accuracy is higher when using the variables with statistical significance.

• ETRI Journal
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• v.34 no.5
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• pp.743-752
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• 2012

Semantic interpretation of the relationship between noun compound (NC) elements has been a challenging issue due to the lack of contextual information, the unbounded number of combinations, and the absence of a universally accepted system for the categorization. The current models require a huge corpus of data to extract contextual information, which limits their usage in many situations. In this paper, a new semantic relations interpreter for NCs based on novel lightweight binary features is proposed. Some of the binary features used are novel. In addition, the interpreter uses a new feature selection method. By developing these new features and techniques, the proposed method removes the need for any huge corpuses. Implementing this method using a modular and plugin-based framework, and by training it using the largest and the most current fine-grained data set, shows that the accuracy is better than that of previously reported upon methods that utilize large corpuses. This improvement in accuracy and the provision of superior efficiency is achieved not only by improving the old features with such techniques as semantic scattering and sense collocation, but also by using various novel features and classifier max entropy. That the accuracy of the max entropy classifier is higher compared to that of other classifiers, such as a support vector machine, a Na$\ddot{i}$ve Bayes, and a decision tree, is also shown.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.