Earticle

신경회로망은 과거 데이터로부터 유용한 정보를 추출해서 주가지수의 이동 방향을 예측하는데 사용되어 왔다. 주가 지수의 상승 또는 하락 방향을 예측하는 기존 연구는 지수의 작은 변화에도 상승이나 하락을 예측하므로 이를 기반으로 지수 연동 ETF를 매매 하면 손실이 발생할 가능성이 높다. 본 논문에서는 ETF 매매 손실을 줄이고 매매 당 일정 이상의 수익을 내기 위한 일별 KOrea composite S0tock Price Index (KOSPI)의 이동 방향을 예측하는 신경회로망 모델을 제안한 다. 제안된 모델은 이동 방향 예측을 위해 전일 대비 지수 변동률이 상승(변동률≥ α), 하락(변동률≤ -α)과 중립(- α < 변동률 > α)을 표시하는 출력을 갖는다. 예측이 상승이면 레버리지 Exchange Traded Fund (ETF)를, 하락이면 인버스 ETF를 매수한다. 본 논문에서 구현된 신경회로망 모델 중 PNN1의 Hit ratio (HR)은 학습에서 0.720, 평가에서 0.616이다. 평가용 데이터로 ETF 매매를 시뮬레이션하면 수익률은 8.39 ~ 16.32 %를 보인다. 또한 제안된 이동 방향 예측 신경회로망 모델이 주가지수 예측 신경회로망 모델 보다 ETF 매매 성공률과 수익률에서 더 우수하다.

Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index≥ α), falling (change rate≤ - α) and neutral (- α < change rate < α). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.

Directions for the development of information and communication technology are changing to the age of the artificial intelligence-based ubiquitous IT technology. Researches have progressed up to recognize out various situations intelligently by the various informations obtained by using a several sensors. Through the development of the internet of things, we want to design an automatic recognition algorithm, and we want to attempt to determine the accuracy of the situation recognition. We want to design the Radial Basis Function Neural Networks(RBFNN) pattern classifier to processing vast amounts of data sequentially using the automatic recognition algorithm. Proposed RBFNN pattern classifier extends to the first-order linear equation, then, it can study through the least square method. And, it shows the pattern classification rules through the certified data, then, it shows the superiority of the pattern classifier by performance index for the situation awareness.

The Clustering is representative method of components classification. The previous clustering methods that use cohesion and coupling cannot be effective because design pattern has focused on relation between classes. In this paper, we classified design patterns with features of object-oriented relationship. The result is that classification by clustering showed higher precision than classification by facet. It is effective that design patterns are classified by automatic clustering algorithm. When patterns are retrieved in classification of design patterns, we can use to compare them because similar pattern is saved to same category. Also we can manage repository efficiently because of storing patterns with link information.

비만은 세계보건기구에서 질병으로 규정하고 있으며, 신체 내외부적인 영향을 나타낸다. 본 연구는 머신러닝을 이용 하여 생활패턴에 따른 비만도를 예측하고자 한다. 머신러닝에 이용한 데이터는 오픈데이터를 사용하였으며, 머신러닝 모델은 구글 코랩, 파이썬을 이용하고 모델 구성은 Light Gradient Boosting Machine, Extreme Gradient Boosting, Decision Tree Classifier, K Neighbors Classifier, Naive bayes 총 5개의 모델로 구성하였다. 각 모델 성능 평가 지표는 정확도, area under curve, 재현율, 정밀도, F1-score로 평가하였다. 해당 데이터를 활용한 5개 모델 중 Light Gradient Boosting Machine이 모든 지표에서 성능이 가장 우수 했으며, 지표에 대한 결과는 정확도 0.9601, area under curve 0.9981, 재현율 0.9601, 정밀도 0.9611, F1 score 0.9601이었다. 본 연구를 통해 기본적인 생활 패턴에 따른 비만도 예측을 통해 비만에 대한 사전 예방이 가능할 것으로 사료된다.

Obesity is defined as a disease by the World Health Organization and indicates internal and external influences on the body. This study aims to predict obesity according to lifestyle patterns using machine learning. The data used for machine learning used open data, and the machine learning model used Google Colab and Python. The model configuration consisted of a total of five models: Light Gradient Boosting Machine, Extreme Gradient Boosting, Decision Tree Classifier, K Neighbors Classifier, and Naive Bayes. The performance evaluation indices for each model were accuracy, area under curve, recall, precision, and F1-score. Among the five models using the data, Light Gradient Boosting Machine showed the best performance in all indices, and the results for the indices were accuracy 0.9601, area under curve 0.9981, recall 0.9601, precision 0.9611, and F1-score 0.9601. Through this study, it is believed that it will be possible to prevent obesity in advance by predicting obesity according to basic lifestyle patterns.

최근 침입 탐지 시스템에 대한 관심이 증대되고 있다. 침입 탐지 시스템에서 침입 여부 확인을 위하여 패턴매칭 기법이 주로 사용된다. 기존의 패턴매칭 기법들은 다양한 공격 패턴들에 대한 패턴 비교 시간이 많이 소요되는 문제점이 있었다. 본 논문에서는 기존의 패턴매 칭 기법들이 가지고 있는 문제점을 해결하기 위하여 새로운 침입 탐지 시스템을 제안한다. 제안한 시스템은 효율적인 패턴 비교를 위하여 룰 패턴을 분류한다. 분류된 패턴은 매칭을 위하여 정형화된 트리로 구현한다. 그러므로, 본 논문에서 제안한 침입 탐지 시스템 모델은 효율적으로 네트워크 침입 탐지를 수행할 수 있다.

Recently, lots of researchers work focused on the intrusion detection system. Pattern matching technique is commonly used to detect the intrusion in the system, However, the method requires a lot of time to match between systems rule and inputted packet data. This paper proposes a new intrusion detection system based on the pattern matching technique. Proposed system reduces the required time for pattern matching by using classified system rule. The classified rule is implemented with a general tree for efficient pattern matching. Thereby, proposed system could perform network intrusion detection efficiently.

본 논문은 서베일런스 네트워크에서 이동하는 객체 추적 시 영상 데이터의 전송량을 감소시키는 신경망 계산 시간의 단축 알고리즘을 제안한다. 객체 검출은 디지털화 연속된 영상으로부터 객체 존재 유무를 판단하고, 객체가 존재할 경우 영상 내 객체의 위치, 방향, 크기 등을 알아내는 기술로 정의된다. 그러나 영상 내의 객체는 위치, 크기, 빛의 방향 및 밝기, 장애물 등의 환경적 변화로 인해 객체 모양이 다양해지므로 정확하고 빠른 검출이 어렵다. 따라서 본 논문에서는 신경망을 사용하여 몇 가지 환경적 조건을 극복한 정확하고 빠른 객체 검출 방법을 제안한다. 검색 영역의 축소는 영상 내 색상 영역의 분할과 차영상을 이용하였고, 주성분 분석을 통해 신경망의 입력 벡터를 축소시킴으로써 신경망 수행 시간과 학습 시간을 단축시켰다. 실시간으로 입력되는 동영상에서 모두 실험하였으며, 색상 영역의 분할을 사용할 경우 입력 영상의 칼라 설정의 유무에 따른 검출 성공률의 차를 보였다. 실험 결과에서 보면 제안하는 방법으로써 객체의 움직임을 탐지하였을 때 기존의 방법보다 30% 정도 더 높은 인식 성능을 보여준다.

This paper proposes algorithm to reduce the computing time in a neural network that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. Object Detection can be defined as follows : Given image sequence, which can forom a digitalized image, the goal of object detection is to determine whether or not there is any object in the image, and if present, returns its location, direction, size, and so on. But object in an given image is considerably difficult because location, size, light conditions, obstacle and so on change the overall appearance of objects, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact object detection which overcomes some restrictions by using neural network. Proposed system can be object detection irrelevant to obstacle, background and pose rapidly. And neural network calculation time is decreased by reducing input vector size of neural network. Principle Component Analysis can reduce the dimension of data. In the video input in real time from a CCTV was experimented and in case of color segment, the result shows different success rate depending on camera settings. Experimental results show proposed method attains 30% higher recognition performance than the conventional method.

This paper gives an overview of the various approaches to designing statistical pattern recognition scheme based on Bayes discrimination rule and the artificial neural networks for rotating machine condition classification. Concerning to Bayes discrimination rule, this paper contains the linear discrimination rule applied to classification into several multivariate normal distributions with common covariance matrices, the quadratic discrimination rule under different covariance matrices. Also we discribes k-nearest neighbor method to directly estimate a posterior probability of each class. Five features are extracted in time domain vibration signals. Employing these five features, statistical pattern classifier and neural networks have been established to detect defects on rotating machine. Four different cases of rotation machine were observed. The effects of k number and neural networks structures on monitoring performance have also been investigated. For the comparison of diagnosis performance of these two method, their recognition success rates are calculated form the test data. The result of experiment which classifies the rotating machine conditions using each method presents that the neural networks shows the highest recognition rate.

The Purposes of this study were to find complementary connectible new factors that analyzed correlation relate of Sasang Constitution and GCM Body Type in Static Posture and Dynamic Hyper/Hypo-mobility Pattern. Method of this study was asymtomatic volunteers 232(unmarried man and women), conducted from September 1 to December 31. In this main study progressing step diagnosised first, Constitution of Sasang medicine after being classified into four groups of Soyangin, Taeumin, Soeumin, Taeyangin diagnosis of GCM Body Type and progressed that related Static Posture and Dynamic Hyper/Hypo mobility Pattern. The results are as follows. Distribution of Sasang Medicine Constitution proved to be Taeyangin 13, Soyangin 66, Taeumin 67, Soeumin 86 respectively. Distribution of GCM Body Type proved I Body Type 72(31.0%), II Body Type 54(23.3%), III Body Type 89(38.4%), IV Body Type 17(7.3%). The distribution of Sasang Constitution according to GCM Body Type was that; I Body Type was distributed in the order Soeumin 34.7%(25), Taeumin 31.9%(23), Soyangin 30.6%(22), Soeumin 34.7%(25) is the most people. II Body Type was distribution of in the order Soeumin 42.6%(23), Soyangin 5.9%(14), Taeumin 24.1%(13), Soeumin 42.6%(23) is the most people. III Body Type was distribution of in the order Soeumin 37.1%(33), Taeumin 30.3%(27), Soyangin 28.1%(25), Soeumin 37.1% is the most people. IV Body Type proved high distribution each of Soeumin 29.4%(5) and Soyangin 29.4%(5). In case of main left side posture character of spine and limbs about I Body Type 72 persons with left scapular and ilium forward tilted pattern proved in the order high distribution iliac crest thigh and scapular high 70(97.2%), gluteal fold high and scapular abduction 69(95.8%), lumbar scoliosis 65(90.3%). Also, in case of right side posture character proved in the order high distribution deep gluteal fold 69(95.8%), umbilical deviation 68(94.4%). Incase of main left side posture character of spine and limbs about n Body Type 54 persons proved in the: order high distribution knee hyperextension 50(92.6%), shoulder deviation 49(90.7%) etc. Also, in case of right side posture character proved in the order high distribution pelvic deviation 53(98.1%), iliac crest thigh 52(96.3%), hip flexion and ankle inversion 51(94.4%) etc. In case of main left side posture character of spine and limbs about III Body Type 89 persons proved in the order high distribution shoulder deviation 87(97.8%), scapular abduction 86(96.6%), scapular high 85(95.5%) etc. And in case of right side posture character proved in the order high distribution pelvic deviation and iliac crest thigh 86(96.6%) etc. In case of main left side posture character of spine and limbs about IV Body Type 17 persons proved in the order high distribution pelvic deviation, lumbar sciliosis and lumbar lordosis increase 15(88.2%) etc. Also, in case of right side posture character proved in the order high distribution wrist dorsiflexion 16(94.1%), thickened achilles tendon etc.

지역 패턴을 정확하게 부호화 하는 방법은 텍스처 분류 연구에 매우 중요한 요소다. 하지만 기존 널리 연구된 LBP기반 방 법들은 잡음에 취약한 근본적인 문제점이 있다. 최근 표정인식 분야에서 에지반응 값과 방향 정보를 활용한 LDP방법이 제안 되었다. LDP방법은 LBP보다 잡음에 강하고 더 많은 정보를 코드에 수용할 수 있는 장점이 있지만 텍스처 분류에 적용하기에 는 치명적인 회전 변화에 민감한 단점이 있다. 본 논문에서는 LDP 방법에 회전 불변 특성을 결합하고 기존 LDP가 가지고 있 던 부호 정보를 수용하지 않은 단점과 밝기 값 차이가 적은 영역에서 의미 없는 코드가 생성되는 단점을 극복한 새로운 지역 패턴 부호화 방법인 Rotation Invariant Local Directional Pattern 방법을 제안한다. 본 논문에서 제안된 방법의 텍스처 분류 성능을 입증하기 위해 널리 사용되는 UIUC, CUReT 데이터 셋에서 텍스처 분류를 수행했다. 그 결과 제안된 RILDP방법이 기존 방법보다 우수한 성능을 보여주었다.

Accurate encoding of local patterns is a very important factor in texture classification. However, LBP based methods w idely studied have fundamental problems that are vulnerable to noise. Recently, LDP method using edge response and dire ction information was proposed in facial expression recognition. LDP is more robust to noise than LBP and can accommod ate more information in it's pattern code, but it has drawbacks that it is sensitive to rotation transforms that are critical to texture classification. In this paper, we propose a new local pattern coding method called Rotation Invariant Local Direc tional Pattern, which combines rotation-invariant transform to LDP. To prove the texture classification performance of the proposed method in this paper, texture classification was performed on the widely used UIUC and CUReT datasets. As a result, the proposed RILDP method showed better performance than the existing methods.

This paper proposes a Multiclass Least Squares Twin Support Vector Machine (MLSTSVM) classifier for multi-class classification problems. The formulation of MLSTSVM is obtained by extending the formulation of recently proposed binary Least Squares Twin Support Vector Machine (LSTSVM) classifier. For M-class classification problem, the proposed classifier seeks M-non parallel hyper-planes, one for each class, by solving M-linear equations. A regularization term is also added to improve the generalization ability. MLSTSVM works well for both linear and non-linear type of datasets. It is relatively simple and fast algorithm as compared to the other existing approaches. The performance of proposed approach has been evaluated on twelve benchmark datasets. The experimental result demonstrates the validity of proposed MLSTSVM classifier as compared to the typical multi-classifiers based on ‘Support Vector Machine’ and ‘Twin Support Vector Machine’. Statistical analysis of the proposed classifier with existing classifiers is also performed by using Friedman’s Test statistic and Nemenyi post hoc techniques.

Bed Activity Monitoring System (BAMS) monitors and assess the mobility of people on a bed. This is a useful and critical application for patients with mobility issues after stroke or traumatic brain injury. The system is based on processing of data collected from a piezoelectric pressure sensor for discriminating mobility patterns. There are four different types of motion that were simulated by non-patient volunteers and data was collected. In this paper, two methods were used to extract feature parameters (autoregressive and cepstral coefficients) from the acquired data. Two classification algorithms, Euclidean Distance Measure (EDM) and Weighted Distance Measure (WDM) were used to classify and discriminate the mobility patterns of normal person (healthy subject) from people with mobility issues (patient subjects). Experimental result shows that the recognition rate using cepstral parameters was more effective compare to autoregressive parameters.

Selecting appropriate feature extraction method is absolutely one of the most important factors to archive high classification performance in pattern recognition systems. Among different feature extraction methods proposed for pattern recognition, statistical moments seem to be so promising. Whereas theoretical comparison of the moments is too complicated, in this paper, an experimental evaluation on four well known statistical moments namely Hu invariant moments, Affine invariant moments, Zernike moments, and Pseudo-Zernike moments is presented. Set of different experiments on a binary images dataset consisting of regular, translated, rotated, and scaled Persian printed numerical characters using a nearest neighbor rule classifier has been done and variety of interesting results have been presented. Finally, the results show that Pseudo-Zernike moments outperform the other introduced moments.

DNA computing is a computing paradigm as information storage media and information processing operators. Especially, information processing of DNA computing has emerged for gene expression pattern analysis and disease diagnosis. Recently, we developed a theoretical model for pattern classification, biomolecular perceptron. In the model, each pattern is a solution of various nucleotide strands. The patterns of the DNA species are summed with corresponding weight factors, where the result is used for the pattern’s classification. The calculation of this weighted summation is performed via competitive hybridization reactions between input molecules and differentially labeled probe molecules (molecular beacon), and the calculation is detected as fluorescence signals. Here, we demonstrate the experimental feasibility of pattern classification. We perform disease type discrimination using the subsequence of the acute leukemia marker genes. The results show the possibility of our method as a pattern classification tool and diagnosis tool using gene expression. Because of the absence of information conversion steps, diagnosis process can be simplified.

We introduce a new category of fuzzy neural networks with multiple-output based on fuzzy clustering algorithm, especially, fuzzy c-means clustering algorithm (FCM-based FNNm) for pattern classification in this paper. The premise part of the rules of the proposed networks is realized with the aid of the scatter partition of input space generated by FCM clustering algorithm. The partitioned local spaces describe the fuzzy rules and the number of the partitioned local spaces is equal to the number of clusters. Due to these characteristics, we may alleviate the problem of the curse of dimensionality. The consequence part of the rules is represented by polynomial functions with multiple-output for pattern classification. And the coefficients of the polynomial functions are learned by back propagation algorithm. To optimize the parameters of the proposed FCM-based FNNm we consider real-coded genetic algorithms. The proposed networks are evaluated with the use of numerical experimentation.

A plethora of big data applications are emerging and being researched in the computer science community which require online classification and pattern recognition of huge data pools collected from sensor networks, image and video systems, online forum platforms, medical agencies etc. However, as an NP hard issue data mining techniques are facing with lots of difficulties. To deal with the hardship, we conduct research on the novel algorithm for data mining and knowledge discovery through network entropy. We firstly introduce necessary data analysis techniques such as support vector machine, neural network and decision tree methods. Later, we analyze the organizational structure of network graphical pattern with the knowledge of machine learning methodology and graph theory. Eventually, our modified method is finalized with decision and validation implementation. The simulation results of our approach on different databases show the feasibility and effectiveness of our proposed framework. As the final part, we provide our conclusion and prospect.

RFID technology has been widely used in many fields. Lots of information could be obtained from the enormous RFID devices. Therefore, classification of the trajectory of these moving objects is significant to predict the trends of the moving objects. This paper introduces an innovative algorithm, using pattern recognition approach. This algorithm could be divided into several steps. First of all, the coarse-fine layer classification approach is used for clustering the trajectories. That aims to cut down the trajectories according to the difference phases. Secondly, in the sub-set of the trajectory, searching set is established through detecting the neighbor domains which have been classified at different phases. Finally, the hierarchical classification approach is used for classifying the sub-trajectory. By using the approach, the experimental results imply the feasibility and practicality of the proposed approach on figuring out the familiar trajectory of the RFID-enabled moving objects. It is observed that With the increasing of neighbor value , the linear trends is obvious from the figure, thus, the neighbor query decreases and the magnification ratio outperforms to TRACLUS method. Additionally, the proposed algorithm uses coarse-fine strategy at phases-to-phases, saving the time spend on large number of distance calculation at various phase.

In this work, we have presented a hierarchical scheme for classifying music data. Instead of dealing with large variety of features, proposed scheme relies on MFCC and its variants which are introduced at the different stages to satisfy the need. At the top level music is classified as song (music with voice) and instrumental (music without voice) based on MFCC. Subsequently, instrumental signals and songs are classified based on instrument type and genres respectively. Hierarchical approach has been followed for such detailed categorization. Using two-stage process, instrumental signals are identified as one of the four types namely, string, woodwind, percussion or keyboard. Wavelet and MFCC based features are used for this purpose. For song classification, at first level signals are categorized as classical or non-classical(popular) ones by capturing the MFCC pattern present in the high sub-band of wavelet decomposed signal. At second level, we consider the task of further classification of popular songs into various genres like Pop, Jazz, Bhangra (an Indian genre) based on amplitude variation pattern. RANSAC has been utilized as the classifier at all stages. Experimental result indicates the effectiveness of the proposed schemes.

Study of different patterns on a local neighborhood of a texture plays an important role in characterization, and classification of the textures. The present paper proposes a method for measuring the occurrence factor of patterns on a randomly thresholded binary image. For this process eight simple patterns are chosen on a 3×3 neighborhood. The simple patterns are chosen in such a way that any complex pattern can be formed by grouping one or more of these simple patterns. The pattern occurrence factor of different binary images is also compared with the actual binary texture image. The experimental results on sixty four textures indicate good comparison of variation of occurrence in these patterns on different binary images of random threshold

청소년들은 ‘위기’를 겪게 되는데, 이 위기를 어떻게 보내느냐에 따라 개개인의 일생에 큰 영향을 미치게 된다. 현대사회는 청소년의 발달과 적응에 장애가 될 수 있는 다양한 위기상황이 존재하므로 기독교 교육적 차원에서 위기의 청소년을 이해하고 도울 수 있는 방안을 모색할 때이다. 선행연구를 통하여 위기청소년의 개념을 정리하였고 그들의 특징 과 문제점을 설명하였다. 현재 학교현장과 교육기관에서 사용되어지는 시스템의 분석을 통해 청소년의 위기유형을 분류하는 기준을 분석하였으며 기독교교육을 통한 가정사역적 접근을 통해 교회에서 품어야 할 위기청소년의 다양한 지원방법을 제시하고자 하였다. 본 연구에서 학생의 위기유형은 크게 세 가지 유형(가정위기, 감정ㆍ정서위기, 학교위 기)로 분류된다. 가정위기는 정서대화, 경제문제, 가정폭력, 결손 및 조손가정의 문제들을 포함하고 감정ㆍ정서위기는 우울, 불안, 충동조절, 스트레스등의 문제들을 포함한다. 이러 한 문제들은 학교위기로 나타나는데, 학생의 개인적인 위기유형으로 우울, 불안, 자해, 자 살등의 정신건강적인 양상들과 인터넷 과몰입, 음주ㆍ흡연의 모습을 보이며, 학생의 사회 적인 문제로는 집단 따돌림, 도벽, 금품갈취, 학교폭력, 학교부적응, 품행장애, ADHD, 성폭 력 등의 사회적 문제들이 야기될 수 있다고 하였다. 청소년의 3가지 위기유형을 극복하기 위하여 긍정적 개인요인을 통한 위기지원, 가정사역에서 가정을 통한 위기지원, 교회 및 사회기관을 통한 위기지원, 전문상담치료를 통한 위기지원으로 제시하였다.

Christian Educational Approach for Crisis Pattern Classification in School Counseling: Family Life Education Mila Park Adjunct Professor, Sungkyul University Every youth has gone through the crisis period, and how to undergo this time has made big influence to their entire life. Modern society has offered various crisis environment to obstacle the youths’ development and adaptation. This study tried to understand and help the youths who faced crisis situations with Christian educational approach. The researcher proposed the concepts of youth crises and their characteristics and problems through the literature reviews. This research classified the students’ crisis patterns with three categories: family crisis, emotional crisis, and school crisis. Family crisis included communication problems, financial problems, violences, parentless family, and single family problems. Emotional crisis included depression, anxiety, impulse control disorder, and stress. Family and emotional crisis problems are turned up to the school crisis problems. Individual student’s crisis patterns appeared in psychiatric and health problems such as depression, anxiety, self-inflicted wound, suicide, Internet addiction, and alcoholic and smoking problems. These problems are connected to the societal problems such as ostracism, kleptomaniac, extortion, violence, school maladjustment, ADHD, sexual violence. 4. 학교상담현장에서의 위기유형분류와 기독교 가정교육(사역)적 접근 107 This research suggested various supporting alternatives to overcome youth students’ three crisis patterns. Affirmative self-esteem, family life educational supporting, local church and social institution supporting, and specialized counseling treatment are proposed to cure the youths’ family, emotional, and school crisis problems.

데이터 마이닝의 문서분류 기술에서 발전된 오피니언 마이닝은 이제 국외뿐만 아니라 국내 산업에서 중요한 관심분야로 자리잡아가고 있다. 오피니언 마이닝의 핵심은 문서에서 감정 단어를 추출하여 긍정/부정 여부를 얼마나 정확하게 판별하느냐를 평가하는 것이다. 국내에서도 이에 관련된 많은 연구가 이루어 졌으나 아직 실용적으로 적용 할 만큼의 분류 정확도를 보이지 않고 있다. 한국어의 경우 비문법적 표현, 감정단어의 다양성 등으로 인해 문서의 극성을 판별하기가 쉽지 않기 때문이다. 본 논문에서는 문법적 요소를 최대한 배제하고 단어패턴의 빈도만을 고려한 새로운 오피니언 문서 분류기법을 제안한다. 제안된 방법에서는 문서를 단어들의 리스트로 추상화한 후, 패턴들의 빈 도를 이용하여 기계학습 알고리즘을 적용한다. 이후에 적절한 스코어 함수를 적용하여 문서의 극성을 판별한다. 또한 제안된 기법의 정확도를 평가하기 위해서 실험결과를 제시한다.

An opinion mining technique which was developed from document classification in area of data mining now becomes a common interest in domestic as well as international industries. The core of opinion mining is to decide precisely whether an opinion document is a positive or negative one. Although many related approaches have been previously proposed, a classification accuracy was not satisfiable enough to applying them in practical applications. A opinion documents written in Korean are not easy to determine a polarity automatically because they often include various and ungrammatical words in expressing subjective opinions. Proposed in this paper is a new approach of classification of opinion documents, which considers only a frequency of word patterns and excludes the grammatical factors as much as possible. In proposed method, we express a document into a bag of words and then apply a learning algorithm using a frequency of word patterns, and finally decide the polarity of the document using a score function. Additionally, we also present the experiment results for evaluating the accuracy of the proposed method.