• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.47-56
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• 2003

Korean delimits words by white-space like English, but words In Korean Is a little different in structure from those in English. Words in English generally consist of one word, but those in Korean are composed of one word and/or morpheme or more. Because of this difference, a word between white-spaces is called an Eojeol in Korean. We propose a method for segmenting and classifying Korean words and/or morphemes based on syllables using an instance-based learning. In this paper, elements of feature sets for the instance-based learning are one previous syllable, one current syllable, two next syllables, a final consonant of the current syllable, and two previous categories. Our method shows more than 97% of the F-measure of word segmentation using ETRI corpus and KAIST corpus.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.2001-2004
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• 2003

비정상 행위의 탐지를 위한 침입탐지 시스템의 성능을 좌우하는 가장 큰 요인들은 패킷의 손실없는 수집과 해당 도메인에 알맞은 분류 기법이라 할 수 있다. 본 논문에서는 기존의 탐지엔진에 적용된 알고리즘의 부류에서 벗어나 Instance 기반의 알고리즘인 IBL(Instance Based Learning)을 선택하여 학습시간의 단축과 패턴생성에 따른 분류근거의 명확성을 고려였다. 또한, 기존 IBL에 포함되어 있는 Symbolic value 의 거리계산 방식에서 네트워크의 로우 데이터인 패킷을 처리하는데 따르는 문제를 해결하기 위해 VDM(Value Difference Matrix)을 사용함으로써 탐지률을 향상시킬 수 있었다. Symbolic value간의 거리계산에 따른 성능향상의 정도를 알아보기 위해 VDM 적용 유무에 따른 실험결과와 탐지엔진에 적용되었던 알고리즘들인 COWEB 과 C4.5를 이용한 결과를 비교분석 하였다.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.44-47
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• 1997

This paper is a study on the hand gesture recognition using Instance-based teaming, Symbolic learning algorithms and Power Glove which supplies information on finger position, hand position and orientation. The data were carefully examined, and a few features of the data that would serve as good discriminants between signs when used with the learning algorithms were extracted. The hand gesture data collected from 5 people were applied to the teaming algorithms. In spite of the noise and accuracy constraints of the equipment used, some accuracy rates were achieved.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.6
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• pp.540-550
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• 2015

There have been many attempts to find knowledge from data using conventional statistics, data mining, artificial intelligence, machine learning and pattern recognition. In those research areas, knowledge is approached in two ways. Firstly, researchers discover knowledge represented in general features for universal recognition, and secondly, they discover exceptional and distinctive features. In process mining, an instance is sequential information bounded by case ID, known as process instance. Here, an exceptional process instance can cause a problem in the analysis and discovery algorithm. Hence, in this paper we develop a method to detect the knowledge of exceptional and distinctive features when performing process mining. We propose a method for anomaly detection named Distance-based Anomaly Process Instance Detection (DAPID) which utilizes distance between process instances. DAPID contributes to a discovery of distinctive characteristic of process instance. For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. Additionally, we experiment on real data from a domestic port terminal to demonstrate our proposed methodology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1348-1375
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• 2018

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filter-based and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.

• Journal of the Korean Society of Safety
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• v.34 no.6
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• pp.50-57
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• 2019

In this study, various types of deep learning models that have been proposed recently are classified according to data input / output types and analyzed to find the deep learning model suitable for constructing a crack detection model. First the deep learning models are classified into image classification model, object segmentation model, object detection model, and instance segmentation model. ResNet-101, DeepLab V2, Faster R-CNN, and Mask R-CNN were selected as representative deep learning model of each type. For the comparison, ResNet-101 was implemented for all the types of deep learning model as a backbone network which serves as a main feature extractor. The four types of deep learning models were trained with 500 crack images taken from real concrete structures and collected from the Internet. The four types of deep learning models showed high accuracy above 94% during the training. Comparative evaluation was conducted using 40 images taken from real concrete structures. The performance of each type of deep learning model was measured using precision and recall. In the experimental result, Mask R-CNN, an instance segmentation deep learning model showed the highest precision and recall on crack detection. Qualitative analysis also shows that Mask R-CNN could detect crack shapes most similarly to the real crack shapes.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.418-434
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• 2018

With the increasing use of the Internet and electronic documents, automatic text categorization becomes imperative. Several machine learning algorithms have been proposed for text categorization. The k-nearest neighbor algorithm (kNN) is known to be one of the best state of the art classifiers when used for text categorization. However, kNN suffers from limitations such as high computation when classifying new instances. Instance selection techniques have emerged as highly competitive methods to improve kNN through data reduction. However previous works have evaluated those approaches only on structured datasets. In addition, their performance has not been examined over the text categorization domain where the dimensionality and size of the dataset is very high. Motivated by these observations, this paper investigates and analyzes the impact of instance selection on kNN-based text categorization in terms of various aspects such as classification accuracy, classification efficiency, and data reduction.

• Annual Conference on Human and Language Technology
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• 2012.10a
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• pp.47-52
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• 2012

본 논문에서는 소셜 네트워크 서비스에서 사용자가 작성한 텍스트로부터 그 사용자 프로파일 식별하는 문제를 다룬다. 프로파일 식별 관련 기존 연구에서는 개별 텍스트를 하나의 학습 단위로 간주하고 이를 기반으로 학습 모델을 구축한다. 프로파일을 식별하고자 하는 사용자의 텍스트들이 주어지면 각 텍스트마다 프로파일을 식별하고, 식별된 결과들을 합쳐 최종 프로파일로 선택한다. 하지만 SNS 특성상 프로파일을 식별하는 데에 영향을 끼치지 않는 텍스트들이 다수 존재하며, 기존 연구들은 이 텍스트들을 특별한 처리없이 학습 및 테스트에 사용함으로 인해 프로파일 식별 성능이 저하되는 문제점이 있다. 본 논문에서는 다중 인스턴스 학습(Multi-Instance Learning)을 기반으로 사용자 프로파일을 식별한다. 제안한 방법은 사용자가 작성한 텍스트 전체, 즉 텍스트 집합을 학습 단위로 간주하고 다중 인스턴스 학습 문제로 변환하여 프로파일을 식별한다. 다중 인스턴스 학습을 사용함으로써 프로파일 식별에 유의미한 텍스트들만이 고려되고 그 결과 프로파일 식별에 영향을 끼치지 않는 텍스트로부터의 성능 하락을 최소화할 수 있다. 실험을 통해 제안한 방법이 기존 학습 방법보다 성별, 나이, 결혼/연애 상태를 식별함에 있어서 더 좋은 성능을 보인다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.283-286
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• 2007

This paper proposes a genetic algorithm (GA) approach to instance selection in case-based reasoning (CBR) for the prediction of Korea Stock Price Index (KOSPI). CBR has been widely used in various areas because of its convenience and strength in complex problem solving. Nonetheless, compared to other machine learning techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. In this paper, the GA optimizes simultaneously feature weights and a selection task for relevant instances for achieving good matching and retrieval in a CBR system. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for instance selection in CBR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4968-4986
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• 2017

In this paper, we primarily address the difficulty of automatic generation of a plausible depth map from a single image in an unstructured environment. The aim is to extrapolate a depth map with a more correct, rich, and distinct depth order, which is both quantitatively accurate as well as visually pleasing. Our technique, which is fundamentally based on a preexisting DepthTransfer algorithm, transfers depth information at the level of superpixels. This occurs within a framework that replaces a pixel basis with one of instance-based learning. A vital superpixels feature enhancing matching precision is posterior incorporation of predictive semantic labels into the depth extraction procedure. Finally, a modified Cross Bilateral Filter is leveraged to augment the final depth field. For training and evaluation, experiments were conducted using the Make3D Range Image Dataset and vividly demonstrate that this depth estimation method outperforms state-of-the-art methods for the correlation coefficient metric, mean log10 error and root mean squared error, and achieves comparable performance for the average relative error metric in both efficacy and computational efficiency. This approach can be utilized to automatically convert 2D images into stereo for 3D visualization, producing anaglyph images that are visually superior in realism and simultaneously more immersive.