• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.7-8
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• 2018

This study proposes an analysis of the Word2vec-based machine learning classifiers for the sake of opinion mining tasks. As a bench-marking method, BOW (Bag-of-Words) was adopted. On the basis of utilizing the Word2vec and BOW as feature extraction methods, we applied Laptop and Restaurant dataset to LR, DT, SVM, RF classifiers. The results showed that the Word2vec feature extraction yields more improved performance.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.2
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• pp.1-11
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• 2017

In this paper, we design and implement PADIL(Prediction And Detection of Information Leakage) system that predicts and detect information leakage behavior of insider by analyzing network traffic and applying a variety of machine learning methods. we defined the five-level information leakage model(Reconnaissance, Scanning, Access and Escalation, Exfiltration, Obfuscation) by referring to the cyber kill-chain model. In order to perform the machine learning for detecting information leakage, PADIL system extracts various features by analyzing the network traffic and extracts the behavioral features by comparing it with the personal profile information and extracts information leakage level features. We tested various machine learning methods and as a result, the DecisionTree algorithm showed excellent performance in information leakage detection and we showed that performance can be further improved by fine feature selection.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.625-627
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• 2005

결정 트리(Decision Tree)는 주어진 데이터의 경향을 학습하는 데 사용되는 대표적인 방식이다. 이것은 주어진 데이터를 구조화하기 위하여 데이터의 속성과 정보의 엔트로피에 기반을 둔 정보획득량을 이용한다. 본 논문에서는 유비쿼터스 환경에서 사용자 프로파일 정보처럼 시간에 따라 그 경향이 변하는 데이터에 유용하게 적용할 수 있는 시간 가중치 엔트로피를 정의한다. 그리고 ID3 알고리즘을 기반으로 새롭게 제안하는 시간 가중치 엔트로피를 이용하는 향상된 ID3 알고리즘을 쓰고 사용자의 경향을 분석한다. 본 논문에서 제안하는 엔트로피를 이용하는 방식은 데이터들의 시간에 관한 영향을 고려해서 기준방식보다 분석결과가 더욱 유리하다. 두 방식의 비교 테스트 결과를 보면 시간 가중치 엔트로피를 이용하는 알고리즘은 기존의 ID3 알고리즘보다 구성된 트리의 구조가 매우 간단하고 유리하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.240-259
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• 2020

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.378-384
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• 2005

In this paper, we describe a novel method of spam-filtering to improve the performance of conventional spam-filtering systems. Conventional systems filter emails by investigating words distribution in email headers or bodies. Nowadays, spammers begin making email accounts in web-based email service sites and sending emails as if they are not spams. Investigating the email accounts of those spams, we notice that there is a large difference between the automatically generated accounts and ordinaries. Based on that difference, incoming emails are classified into spam/non-spam classes. To classify emails from only account strings, we used decision trees, which have been generally used for conventional pattern classification problems. We collected about 2.15 million account strings from email service sites, and our account checker resulted in the accuracy of $96.3\%$. The previous filter system with the checker yielded the improved filtering performance.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.435-446
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• 2020

With the ability to learn rules from training data, the machine learning model can classify unknown objects. At the same time, the dimension of hyperspectral data is usually large, which may cause an over-fitting problem. In this research, an identification methodology of tea diseases was proposed based on spectral reflectance and machine learning, including the feature selector based on the decision tree and the tea disease recognizer based on random forest. The proposed identification methodology was evaluated through experiments. The experimental results showed that the recall rate and the F1 score were significantly improved by the proposed methodology in the identification accuracy of tea disease, with average values of 15%, 7%, and 11%, respectively. Therefore, the proposed identification methodology could make relatively better feature selection and learn from high dimensional data so as to achieve the non-destructive and efficient identification of different tea diseases. This research provides a new idea for the feature selection of high dimensional data and the non-destructive identification of crop diseases.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.150-155
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• 2014

본 논문에서는, 원거리 음성인식을 위한 서브밴드 필터링 기반의 빠르고 효율적인 에코제거 시스템을 제안한다. 제안하는 에코제거 시스템은 우선 채널간 유사도 (correlation) 가 높을 경우 적응필터가 오작동하는 것을 방지하기 위해 spatial decorrelation 을 적용하게 된다. 그리고 tree 형태를 가지는 IIR filterbank 기반의 subband 구조를 채택함으로써, 적은 차수로도 효과적인 analysis, synthesis 필터링을 수행할 수 있도록 한다. 이 과정에서 불가피하게 발생하는 서브 밴드간 spectral aliasing은 notch filter를 적용해 해결할 수 있다. 또한 적응 필터로는 improved proportionate normalized least-mean-square (IP-NLMS) 알고리즘을 사용해 수렴속도 및 에코제거 성능에서 우수함을 확인하였다. 마지막으로 decision-directed estimation 기반의 residual echo suppressor를 적용해 잔여 에코를 제거하게 된다. 본 논문에서는 각 단계를 구성하게 된 이론적인 배경을 소개하고, 실제 에코가 존재하는 환경에서 ERLE, 원거리 음성 인식률, computational complexity를 통해 제안하는 에코제거 시스템의 효과를 입증하도록 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3725-3748
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• 2018

Text data distribution is often imbalanced. Imbalanced data is one of the challenges in text classification, as it leads to the loss of performance of classifiers. Many studies have been conducted so far in this regard. The proposed solutions are divided into several general categories, include sampling-based and algorithm-based methods. In recent studies, feature selection has also been considered as one of the solutions for the imbalance problem. In this paper, a novel one-sided feature selection known as probabilistic feature selection (PFS) was presented for imbalanced text classification. The PFS is a probabilistic method that is calculated using feature distribution. Compared to the similar methods, the PFS has more parameters. In order to evaluate the performance of the proposed method, the feature selection methods including Gini, MI, FAST and DFS were implemented. To assess the proposed method, the decision tree classifications such as C4.5 and Naive Bayes were used. The results of tests on Reuters-21875 and WebKB figures per F-measure suggested that the proposed feature selection has significantly improved the performance of the classifiers.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.709-719
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• 2022

Recently, as the importance of environmental protection has emerged, interest in new and renewable energy is also increasing worldwide. In particular, the solar energy sector accounts for the highest production rate among new and renewable energy in Korea due to its infinite resources, easy installation and maintenance, and eco-friendly characteristics such as low noise emission levels and less pollutants during power generation. However, although climate prediction is essential since solar power is affected by weather and climate change, solar radiation, which is closely related to solar power, is not currently forecasted by the Korea Meteorological Administration. Solar radiation prediction can be the basis for establishing a reasonable new and renewable energy operation plan, and it is very important because it can be used not only in solar power but also in other fields such as power consumption prediction. Therefore, this study was conducted for the purpose of improving the accuracy of solar radiation. Solar radiation was predicted by a total of three weather variables, temperature, humidity, and cloudiness, and solar radiation outside the atmosphere, and the results were compared using various models. The CatBoost model was best obtained by fitting and comparing the Boosting series (XGB, CatBoost) and RNN series (Simple RNN, LSTM, GRU) models. In addition, the results were further improved through Time series cross-validation.