• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.41-48
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• 2019

In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.149-155
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• 2023

In urban areas it can be difficult to utilize global navigation satellite systems (GNSS) due to signal reflections and blockages. It is thus crucial to detect reflected or blocked signals because they lead to significant degradation of GNSS positioning accuracy. In a previous study, a classifier for global positioning system (GPS) signal reception conditions was developed using three features and the support vector machine (SVM) algorithm. However, this classifier had limitations in its classification performance. Therefore, in this study, we developed an improved machine learning based method of classifying GPS signal reception conditions by including an additional feature with the existing features. Furthermore, we applied various machine learning classification algorithms. As a result, when tested with datasets collected in different environments than the training environment, the classification accuracy improved by nine percentage points compared to the existing method, reaching up to 58%.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.817-824
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• 2003

We employed a hierarchical document classification method to classify a massive collection of electronic medical records(EMR) written in both Korean and English. Our experimental system has been learned from 5,000 records of EMR text data and predicted a newly given set of EMR text data over 68% correctly. We expect the accuracy rate can be improved greatly provided a dictionary of medical terms or a suitable medical thesaurus. The classification system might play a key role in some clinical decision support systems and various interpretation systems for clinical data.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.830-839
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• 2017

Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.

• Journal of Information Processing Systems
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• v.3 no.2
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• pp.73-81
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• 2007

In this paper, we introduce a domain-independent classification framework based on both k-nearest neighbor and Naive Bayesian classification algorithms. The architecture of our system is simple and modularized in that each sub-module of the system could be changed or improved efficiently. Moreover, it provides various feature selection mechanisms to be applied to optimize the general-purpose classifiers for a specific domain. As for the enhanced classification performance, our system provides conditional probability boosting (CPB) mechanism which could be used in various domains. In the mood classification domain, our optimized framework using the CPB algorithm showed 1% of improvement in precision and 2% in recall compared with the baseline.

• Journal of Korean Library and Information Science Society
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• v.40 no.4
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• pp.401-425
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• 2009

This study is intended to present methods improving the classification system of KDC architecture engineering fields after comparing and analyzing the academic system of architecture engineering, classification system of KDC, DDC, and LCC, and that of the research field classification system of National Research Foundation of Korea. The results of the analysis have revealed that it is required to improve and correct the KDC 5th edition of architectural engineering including the addition of classification items that reflect the trend of academic development, proper development in the rank classification terms of architectural structure engineering, addition of detailed subjects, selection of proper classification terms, errors of classification symbols and English expression, and omission of correlative indexes in the classification items. This study has proposed improved methods to solve those problems.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.500-509
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• 2022

With the rapid development of educational informatization, teaching methods become diversified characteristics, but a large number of information data restrict the evaluation on teaching subject and object in terms of the effect of English education. Therefore, this study adopts the concept of incremental learning and eigenvalue interval algorithm to improve the weighted decision tree, and builds an English education effect evaluation model based on association rules. According to the results, the average accuracy of information classification of the improved decision tree algorithm is 96.18%, the classification error rate can be as low as 0.02%, and the anti-fitting performance is good. The classification error rate between the improved decision tree algorithm and the original decision tree does not exceed 1%. The proposed educational evaluation method can effectively provide early warning of academic situation analysis, and improve the teachers' professional skills in an accelerated manner and perfect the education system.

• Journal of the Ergonomics Society of Korea
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• v.23 no.3
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• pp.13-24
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• 2004

Anthropometric data is a fundamental resource in developing ergonomic products and workplaces. However, designers often experience difficulty in searching anthropometric data relevant to the design due to the technicality of anthropometric terminologies, ambiguity in the description of measurement method for some anthropometric variables, and inefficiency of existing search methods for anthropometric data. The present study suggests a method to develop a classification system of anthropometric variables for systematic, efficient search of anthropometric data. The proposed method first classifies anthropometric variables according to body segment and type of variable, and then arranges anthropometric variables of the same body segment and variable type by comparing the heights of their reference points. The proposed classification method was applied to establish a classification system of 66 anthropometric variables that were selected for an automotive interior design. Then the established anthropometric classification system was utilized to design a search interface of a web-based anthropometric data retrieval system.

• Journal of Korean Library and Information Science Society
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• v.41 no.4
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• pp.5-33
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• 2010

This study is intended to present methods improving the classification system of KDC education fields after comparing and analyzing the academic system of education, classification system of KDC, NDC, DDC and LCC, and that of the research field classification system of National Research Foundation of Korea. The results of the analysis have revealed that it is required to improve and correct the KDC 5th edition of education including the addition of classification items that reflect the trend of academic development, proper development in the rank classification terms of education detailed fields, addition of detailed subjects, errors of classification symbols and omission of correlative indexes in the classification items. This study has proposed improved methods to solve those problems.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.403-413
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• 2022

Classification and analysis are improved factors for the realtime automation system. In the field of agriculture, the cultivation of different paddy crop depends on the atmosphere and the soil nature. We need to analyze the moisture level in the area to predict the type of paddy that can be cultivated. For this process, Ensemble Modulation Pattern system and Block Probability Neural Network based classification models are used to analyze the moisture and temperature of land area. The dataset consists of the collections of moisture and temperature at various data samples for a land. The Ensemble Modulation Pattern based feature analysis method, the extract of the moisture and temperature in various day patterns are analyzed and framed as the pattern for given dataset. Then from that, an improved neural network architecture based on the block probability analysis are used to classify the data pattern to predict the class of paddy crop according to the features of dataset. From that classification result, the measurement of data represents the type of paddy according to the weather condition and other features. This type of classification model assists where to plant the crop and also prevents the damage to crop due to the excess of water or excess of temperature. The result analysis presents the comparison result of proposed work with the other state-of-art methods of data classification.