• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.267-270
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• 2007

기계학습에서 분류기들의 집합으로 구성된 앙상블 분류기는 단일 분류기에 비해 정확도가 높다는 것이 입증되었다. 본 논문에서는 새로운 앙상블 학습으로서 데이터의 지역 기반 분류기들의 앙상블 학습을 제시하여 기존의 앙상블 학습과의 비교를 통해 성능을 검증하고자 한다. 지역 기반 분류기의 앙상블 학습은 데이터의 분포가 지역에 따라 다르다는 점에 착안하여 학습 데이터를 분할하고 해당하는 지역에 기반을 둔 분류기들을 만들어 나간다. 이렇게 만들어진 분류기들로부터 지역에 따라 가중치를 둔 투표를 하여 앙상블 방법을 이끌어낸다. 본 논문에서 제시한 앙상블 분류기의 성능평가를 위해 UCI Machine Learning Repository에 있는 11개의 데이터 셋을 이용하여 단일 분류기와 기존의 앙상블 분류기인 배깅과 부스팅등의 정확도를 비교하였다. 그 결과 기본 분류기로 나이브 베이즈와 SVM을 사용했을 때 새로운 앙상블 방법이 다른 방법보다 좋은 성능을 보이는 것을 알 수 있었다.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.131-138
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• 2022

Soft sensors are used to anticipate complicated model parameters using data from classifiers that are comparatively easy to gather. The goal of this study is to use artificial intelligence techniques to design and build soft sensors. The combination of a Long Short-Term Memory (LSTM) network and Grey Wolf Optimization (GWO) is used to create a unique soft sensor. LSTM is developed to tackle linear model with strong nonlinearity and unpredictability of manufacturing applications in the learning approach. GWO is used to accomplish input optimization technique for LSTM in order to reduce the model's inappropriate complication. The newly designed soft sensor originally brought LSTM's superior dynamic modeling with GWO's exact variable selection. The performance of our proposal is demonstrated using simulations on real-world datasets.

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.610-621
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• 2021

The soil creep, primarily caused by earthquakes and torrential rainfall events, has widely occurred across the country. The Korea Forest Service attempted to quantify the soil creep susceptible areas using a discriminant value table to prevent or mitigate casualties and/or property damages in advance. With the advent of advanced computer technologies, machine learning-based classification models have been employed for managing mountainous disasters, such as landslides and debris flows. This study aims to quantify the soil creep susceptibility using several classifiers, namely the k-Nearest Neighbor (k-NN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) models. To develop the classification models, we downscaled 292 data from 4,618 field survey data. About 70% of the selected data were used for training, with the remaining 30% used for model testing. The developed models have the classification accuracy of 0.727 for k-NN, 0.750 for NB, 0.807 for RF, and 0.750 for SVM against test datasets representing 30% of the total data. Furthermore, we estimated Cohen's Kappa index as 0.534, 0.580, 0.673, and 0.585, with AUC values of 0.872, 0.912, 0.943, and 0.834, respectively. The machine learning-based classifications for soil creep susceptibility were RF, NB, SVM, and k-NN in that order. Our findings indicate that the machine learning classifiers can provide valuable information in establishing and implementing natural disaster management plans in mountainous areas.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.15-21
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• 2018

In this study, we try to detect anomalies on the network intrusion detection system by learning only one class. We use KDD CUP 1999 dataset, an intrusion detection dataset, which is used to evaluate classification performance. One class classification is one of unsupervised learning methods that classifies attack class by learning only normal class. When using unsupervised learning, it difficult to achieve relatively high classification efficiency because it does not use negative instances for learning. However, unsupervised learning has the advantage for classifying unlabeled data. In this study, we use one class classifiers based on support vector machines and density estimation to detect new unknown attacks. The test using the classifier based on density estimation has shown relatively better performance and has a detection rate of about 96% while maintaining a low FPR for the new attacks.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.290-297
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• 2024

We investigate the efficacy of ensemble learning methods, specifically the soft voting technique, for enhancing heart disease prediction accuracy. Our study uniquely combines Logistic Regression, SVM with RBF Kernel, and Random Forest models in a soft voting ensemble to improve predictive performance. We demonstrate that this approach outperforms individual models in diagnosing heart disease. Our research contributes to the field by applying a well-curated dataset with normalization and optimization techniques, conducting a comprehensive comparative analysis of different machine learning models, and showcasing the superior performance of the soft voting ensemble in medical diagnosis. This multifaceted approach allows us to provide a thorough evaluation of the soft voting ensemble's effectiveness in the context of heart disease prediction. We evaluate our models based on accuracy, precision, recall, F1 score, and Area Under the ROC Curve (AUC). Our results indicate that the soft voting ensemble technique achieves higher accuracy and robustness in heart disease prediction compared to individual classifiers. This study advances the application of machine learning in medical diagnostics, offering a novel approach to improve heart disease prediction. Our findings have significant implications for early detection and management of heart disease, potentially contributing to better patient outcomes and more efficient healthcare resource allocation.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.506-512
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• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The FCS is based on the fuzzy controller system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. In this paper, the FCS modifies input message to fuzzified message and stores those in the message list. The FCS constructs rule-base through matching between messages of message list and classifiers of fuzzy classifier list. The FCS verifies the effectiveness of classifiers using Bucket Brigade algorithm. Also the FCS employs the Genetic Algorithms to generate new rules and modifY rules when performance of the system needs to be improved. Then the FCS finds the set of the effective rules. We will verifY the effectiveness of the poposed FCS by applying it to Autonomous Mobile Robot avoiding the obstacle and reaching the goal.

• Journal of KIISE
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• v.45 no.1
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• pp.22-29
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• 2018

Over the decades, neural networks have been successfully used in numerous applications from speech recognition to image classification. However, these neural networks cannot explain their results and one needs to know how and why a specific conclusion was drawn. Most studies focus on extracting binary rules from neural networks, which is often impractical to do, since data sets used for machine learning applications contain continuous values. To fill the gap, this paper presents an algorithm to extract logic rules from a trained neural network for data with continuous attributes. It uses hyperplane-based linear classifiers to extract rules with numeric values from trained weights between input and hidden layers and then combines these classifiers with binary rules learned from hidden and output layers to form non-linear classification rules. Experiments with different datasets show that the proposed approach can accurately extract logical rules for data with nonlinear continuous attributes.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.5
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• pp.1-10
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• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The FCS is based on the fuzzy controller system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. In this paper, the FCS modifies input message to fuzzified message and stores those in the message list. The FCS constructs rule-base through matching between messages of message list and classifiers of fuzzy classifier list. The FCS verifies the effectiveness of classifiers using Bucket Brigade algorithm. Also the FCS employs the Genetic Algorithms to generate new rules and modify rules when performance of the system needs to be improved. Then the FCS finds the set of the effective rules. We will verify the effectiveness of the poposed FCS by applying it to Autonomous Mobile Robot avoiding the obstacle and reaching the goal.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.359-366
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• 2021

A number of construction companies in Korea invest considerable human and financial resources to construct a system for managing apartment defect data and for categorizing repair tasks. Thus, this study proposes machine learning models to automatically classify defect complaint text-data into one of the sub categories of 'finishing work' (i.e., one of the defect repair tasks). In the proposed models, we employed two word representation methods (Bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF)) and two machine learning classifiers (Support Vector Machine, Random Forest). In particular, we conducted both binary- and multi- classification tasks to classify 9 sub categories of finishing work: home appliance installation work, paperwork, painting work, plastering work, interior masonry work, plaster finishing work, indoor furniture installation work, kitchen facility installation work, and tiling work. The machine learning classifiers using the TF-IDF representation method and Random Forest classification achieved more than 90% accuracy, precision, recall, and F1 score. We shed light on the possibility of constructing automated defect classification systems based on the proposed machine learning models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.141-143
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• 2021

The Activity patterns of animal species are difficult to access and the behavior of freely moving individuals can not be assessed by direct observation. As it has become large challenge to understand the activity pattern of animals such as dogs, and cats etc. One approach for monitoring these behaviors is the continuous collection of data by human observers. Therefore, in this study we assess the activity patterns of dog using the wearable sensors data such as accelerometer and gyroscope. A wearable, sensor -based system is suitable for such ends, and it will be able to monitor the dogs in real-time. The basic purpose of this study was to develop a system that can detect the activities based on the accelerometer and gyroscope signals. Therefore, we purpose a method which is based on the data collected from 10 dogs, including different nine breeds of different sizes and ages, and both genders. We applied six different state-of-the-art classifiers such as Random forests (RF), Support vector machine (SVM), Gradient boosting machine (GBM), XGBoost, k-nearest neighbors (KNN), and Decision tree classifier, respectively. The Random Forest showed a good classification result. We achieved an accuracy 86.73% while the detecting the activity.