• Korean Journal of Computational Design and Engineering
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• v.1 no.3
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• pp.242-256
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• 1996

Although feature-based design is a promising approach to fully integrating CAD/CAM, current feature-based design approaches seldom provide methodologies to easily define and design features. This paper proposes a new approach to integrating parametric design with feature-based design to overcome those limitations by globally decomposing a design into a set of features and locally defining and positioning each feature by geometric constraints. Each feature is defined as a parametric shape which consists of a feature section, attributes, and a set of constraints. The generalized sketching and sweeping techniques are used to simplify the process of designing features. The proposed approach is knowledge-based and its computational efficiency in geometric reasoning is improved greatly. Parametrically designed features not only have the advantage of allowing users to efficiently perform design changes, but also provide designers with a natural design environment in which they can do their work more naturally and creatively.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.666-670
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• 2006

This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.7-13
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• 2018

Recent advancement in data gathering technique improves the capability of information collecting, thus allowing the learning process between gathered data patterns and application sub-tasks. A pattern can be associated with multiple labels, demanding multi-label learning capability, resulting in significant attention to multi-label feature selection since it can improve multi-label learning accuracy. However, existing evolutionary multi-label feature selection methods suffer from ineffective search process. In this study, we propose a evolutionary search process for the task of multi-label feature selection problem. The proposed method creates large set of offspring or new feature subsets and then retains the most promising feature subset. Experimental results demonstrate that the proposed method can identify feature subsets giving good multi-label classification accuracy much faster than conventional methods.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.75-84
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• 2014

In this paper, we dealt with feature selection problem of large-scale and high-dimensional biological data such as omics data. For this problem, most of the previous approaches used simple score function to reduce the number of original variables and selected features from the small number of remained variables. In the case of methods that do not rely on filtering techniques, they do not consider the interactions between the variables, or generate approximate solutions to the simplified problem. Unlike them, by combining set covering and clustering techniques, we developed a new method that could deal with total number of variables and consider the combinatorial effects of variables for selecting good features. To demonstrate the efficacy and effectiveness of the method, we downloaded gene expression datasets from TCGA (The Cancer Genome Atlas) and compared our method with other algorithms including WEKA embeded feature selection algorithms. In the experimental results, we showed that our method could select high quality features for constructing more accurate classifiers than other feature selection algorithms.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.103-106
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• 2023

코로나 팬데믹 사태로 인해 업무환경이 재택근무를 하는 환경으로 바뀌고 악성코드의 변종 또한 빠르게 발전하고 있다. 악성코드를 분석하고 백신 프로그램을 만들면 새로운 변종 악성코드가 생기고 변종에 대한 백신프로그램이 만들어 질 때까지 변종된 악성코드는 사용자에게 위협이 된다. 본 연구에서는 머신러닝 알고리즘을 사용하여 악성파일 여부를 예측하는 방법을 제시하였다. 일반적인 악성코드의 구조를 갖는 Portable Executable 구조 파일을 파이썬의 LIEF 라이브러리를 사용하여 Certificate, Imports, Opcode 등 3가지 feature에 대해 정적분석을 하였다. 학습 데이터로는 정상파일 320개와 악성파일 530개를 사용하였다. Certificate는 hasSignature(디지털 서명정보), isValidcertificate(디지털 서명의 유효성), isNotExpired(인증서의 유효성)의 feature set을 사용하고, Imports는 Import Address Table의 function 빈도수를 비교하여 feature set을 구축하였다. Opcode는 tri-gram으로 추출하여 빈도수를 비교하여 feature set을 구축하였다. 테스트 데이터로는 정상파일 360개 악성파일 610개를 사용하였으며 Feature set을 사용하여 random forest, decision tree, bagging, adaboost 등 4가지 머신러닝 알고리즘을 대상으로 성능을 비교하였고, bagging 알고리즘에서 약 0.98의 정확도를 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.3
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• pp.575-591
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• 2011

We present a novel active contour-based two-pass approach to extract smooth feature regions from a triangular mesh. In the first pass, an active contour formulated in level-set surfaces is devised to extract feature regions with rough boundaries. In the second pass, the rough boundary curve is smoothed by minimizing internal energy, which is derived from its curvature. The separation of the extraction and smoothing process enables us to extract feature regions with smooth boundaries from a triangular mesh without user's initial model. Furthermore, smooth feature curves can also be obtained by skeletonizing the smooth feature regions. We tested our algorithm on facial models and proved its excellence.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.547-550
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• 2009

The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation (BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.647-652
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• 2006

This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive Feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.466-471
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• 2006

IAn important problem of pattern recognition is to extract or select feature set, which is included in the pre-processing stage. In order to extract feature set, Principal component analysis has been usually used and SFS(Sequential Forward Selection) and SBS(Sequential Backward Selection) have been used as a feature selection method. This paper applies genetic algorithm which is a popular method for nonlinear optimization problem to the feature selection problem. So, we call it Genetic Algorithm Feature Selection(GAFS) and this algorithm is compared to other methods in the performance aspect.

• Genomics & Informatics
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• v.18 no.1
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• pp.2.1-2.11
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• 2020

In this paper, we propose a new approach to detecting outliers in a set of segmented genomes of the flu virus, a data set with a heterogeneous set of sequences. The approach has the following computational phases: feature extraction, which is a mapping into feature space, alignment-free distance measure to measure the distance between any two segmented genomes, and a mapping into distance space to analyze a quantum of distance values. The approach is implemented using supervised and unsupervised learning modes. The experiments show robustness in detecting outliers of the segmented genome of the flu virus.