• Title/Summary/Keyword: one-class classification

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Classification of Imbalanced Data Using Multilayer Perceptrons (다층퍼셉트론에 의한 불균현 데이터의 학습 방법)

  • Oh, Sang-Hoon
    • The Journal of the Korea Contents Association
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    • v.9 no.7
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    • pp.141-148
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    • 2009
  • Recently there have been many research efforts focused on imbalanced data classification problems, since they are pervasive but hard to be solved. Approaches to the imbalanced data problems can be categorized into data level approach using re-sampling, algorithmic level one using cost functions, and ensembles of basic classifiers for performance improvement. As an algorithmic level approach, this paper proposes to use multilayer perceptrons with higher-order error functions. The error functions intensify the training of minority class patterns and weaken the training of majority class patterns. Mammography and thyroid data-sets are used to verify the superiority of the proposed method over the other methods such as mean-squared error, two-phase, and threshold moving methods.

A Study On the Integration Reasoning of Rule-Base and Case-Base Using Rough Set (라프집합을 이용한 규칙베이스와 사례베이스의 통합 추론에 관한 연구)

  • Jin, Sang-Hwa;Chung, Hwan-Mook
    • The Transactions of the Korea Information Processing Society
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    • v.5 no.1
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    • pp.103-110
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    • 1998
  • In case of traditional Rule-Based Reasoning(RBR) and Case-Based Reasoning(CBR), although knowledge is reasoned either by one of them or by the integration of RBR and CBR, there is a problem that much time should be consumed by numerous rules and cases. In order to improve this time-consuming problem, in this paper, a new type of reasoning technique, which is a kind of integration of reduced RB and CB, is to be introduced. Such a new type of reasoning uses Rough Set, by which we can represent multi-meaning and/or random knowledge easily. In Rough Set, solution is to be obtained by its own complementary rules, using the process of RB and CB into equivalence class by the classification and approximation of Rough Set. and then using reduced RB and CB through the integrated reasoning.

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Surgical Treatment of Infective Endocarditis (감염성 심내막염에 대한 외과적치료)

  • Wang, Ok-Bo;Park, Ju-Cheol
    • Journal of Chest Surgery
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    • v.25 no.10
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    • pp.1055-1060
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    • 1992
  • Clinical experience of 21 patients with infective endocarditis was reviewed. Endocarditis involved the left-sided valve in 16 cases, the right-sided valve in 2, and PDA in the remaining 3 patients. Valve abnormalities included leaflet perforation in 9 patients, chordal rupture in 2,; annular abscess in 6; and aorticoleft atnal perforation in 2. Sixteen patients underwent valve replacement[aortic valve replacement in 7 patients, mitral replacement in 4 and double valve replacement in 5], two had VSD closure with pulmonary valve excision, three had ductus arteriousus closure. The patients were classified into two groups. I ] Healed endocarditis group: including the patients who had completed a planned cou-rseof antibiotic therapy[N=10], II ] Active endocarditis group: patients in which operations were performed prior to completetion of antibiotic treatment course[N=11]. The indications for operation included congestive heart failure, embolism, and persistent sepsis. Organisms were predominantly streptococcus[N=5] and staphylococcus [N=4] followed by candida, moraxella, and E-coli. By NYHA functional classification, all patients were in Class III or IV preoperatively. There was only one operative mortality in patient from group II. All patients substantially, improved postoperatively with NYHA classification in class I or II. This study shows that early surgical intervention in patients with active endocarditis has desirable outcome.

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A hybrid intrusion detection system based on CBA and OCSVM for unknown threat detection (알려지지 않은 위협 탐지를 위한 CBA와 OCSVM 기반 하이브리드 침입 탐지 시스템)

  • Shin, Gun-Yoon;Kim, Dong-Wook;Yun, Jiyoung;Kim, Sang-Soo;Han, Myung-Mook
    • Journal of Internet Computing and Services
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    • v.22 no.3
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    • pp.27-35
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    • 2021
  • With the development of the Internet, various IT technologies such as IoT, Cloud, etc. have been developed, and various systems have been built in countries and companies. Because these systems generate and share vast amounts of data, they needed a variety of systems that could detect threats to protect the critical data contained in the system, which has been actively studied to date. Typical techniques include anomaly detection and misuse detection, and these techniques detect threats that are known or exhibit behavior different from normal. However, as IT technology advances, so do technologies that threaten systems, and these methods of detection. Advanced Persistent Threat (APT) attacks national or companies systems to steal important information and perform attacks such as system down. These threats apply previously unknown malware and attack technologies. Therefore, in this paper, we propose a hybrid intrusion detection system that combines anomaly detection and misuse detection to detect unknown threats. Two detection techniques have been applied to enable the detection of known and unknown threats, and by applying machine learning, more accurate threat detection is possible. In misuse detection, we applied Classification based on Association Rule(CBA) to generate rules for known threats, and in anomaly detection, we used One-Class SVM(OCSVM) to detect unknown threats. Experiments show that unknown threat detection accuracy is about 94%, and we confirm that unknown threats can be detected.

An Analytical Study on Performance Factors of Automatic Classification based on Machine Learning (기계학습에 기초한 자동분류의 성능 요소에 관한 연구)

  • Kim, Pan Jun
    • Journal of the Korean Society for information Management
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    • v.33 no.2
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    • pp.33-59
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    • 2016
  • This study examined the factors affecting the performance of automatic classification for the domestic conference papers based on machine learning techniques. In particular, In view of the classification performance that assigning automatically the class labels to the papers in Proceedings of the Conference of Korean Society for Information Management using Rocchio algorithm, I investigated the characteristics of the key factors (classifier formation methods, training set size, weighting schemes, label assigning methods) through the diversified experiments. Consequently, It is more effective that apply proper parameters (${\beta}$, ${\lambda}$) and training set size (more than 5 years) according to the classification environments and properties of the document set. and If the performance is equivalent, I discovered that the use of the more simple methods (single weighting schemes) is very efficient. Also, because the classification of domestic papers is corresponding with multi-label classification which assigning more than one label to an article, it is necessary to develop the optimum classification model based on the characteristics of the key factors in consideration of this environment.

Extensions of LDA by PCA Mixture Model and Class-wise Features (PCA 혼합 모형과 클래스 기반 특징에 의한 LDA의 확장)

  • Kim Hyun-Chul;Kim Daijin;Bang Sung-Yang
    • Journal of KIISE:Software and Applications
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    • v.32 no.8
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    • pp.781-788
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    • 2005
  • LDA (Linear Discriminant Analysis) is a data discrimination technique that seeks transformation to maximize the ratio of the between-class scatter and the within-class scatter While it has been successfully applied to several applications, it has two limitations, both concerning the underfitting problem. First, it fails to discriminate data with complex distributions since all data in each class are assumed to be distributed in the Gaussian manner; and second, it can lose class-wise information, since it produces only one transformation over the entire range of classes. We propose three extensions of LDA to overcome the above problems. The first extension overcomes the first problem by modeling the within-class scatter using a PCA mixture model that can represent more complex distribution. The second extension overcomes the second problem by taking different transformation for each class in order to provide class-wise features. The third extension combines these two modifications by representing each class in terms of the PCA mixture model and taking different transformation for each mixture component. It is shown that all our proposed extensions of LDA outperform LDA concerning classification errors for handwritten digit recognition and alphabet recognition.

Mapping of land cover using QuickBird satellite data based on object oriented and ISODATA classification methods - A comparison for micro level planning (Quickbird 영상을 이용한 객체지향 및 ISODATA 분류기법기반 토지피복분류-세부레벨계획을 위한 비교분석)

  • Jayakumar, S.;Lee, Jung-Bin;Heo, Joon
    • Proceedings of the KSRS Conference
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    • 2007.03a
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    • pp.113-119
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    • 2007
  • This article deals mainly with two objectives viz, 1) the potentiality of very high-resolution(VHR) multi-spectral and pan chromatic QuickBird satellite data in resources mapping over moderate resolution satellite data (IRS LISS III) and 2) the advantages of using object oriented classification method of eCognition software in land use and land cover analysis over the ISODATA classification method. These VHR data offers widely acceptable metric characteristics for cartographic updating and increase our ability to map land use in geometric detail and improve accuracy of local scale investigations. This study has been carried out in the Sukkalampatti mini-watershed, which is situated in the Eastern Ghats of Tamil Nadu, India. The eCognition object oriented classification method succeeded in most cases to achieve a high percentage of right land cover class assignment and it showed better results than the ISODATA pixel based one, as far as the discrimination of land cover classes and boundary depiction is concerned.

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A Clustering Approach for Feature Selection in Microarray Data Classification Using Random Forest

  • Aydadenta, Husna;Adiwijaya, Adiwijaya
    • Journal of Information Processing Systems
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    • v.14 no.5
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    • pp.1167-1175
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    • 2018
  • Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed simultaneously. However, microarray data have very little sample data and high data dimensionality. Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction can eliminate redundancy of data; thus, features used in classification are features that only have a high correlation with their class. There are two types of dimensional reduction, namely feature selection and feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection. The proposed approach can be used to categorize features that have the same characteristics in one cluster, so that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is therefore higher than the approach using Random Forest without clustering.

Nondestructive Internal Defects Evaluation for Pear Using NIR/VIS Transmittance Spectroscopy

  • Ryu, D.S.;Noh, S.H.;Hwnag, H.
    • Agricultural and Biosystems Engineering
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    • v.4 no.1
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    • pp.1-7
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    • 2003
  • Internal defects such as browning of the flesh and blackening and rot of the ovary of pear can be easily developed because of the inadequate environmental conditions during the storage and distribution of fruit. The quality assurance system for the agricultural product is to be settled in Korea. All defected agricultural products should be excluded prior to the distribution to enhance the commercial values. However, early stage on-line defect detection of agricultural product is very difficult and even more difficult in a case of the internal defects. The goal of this research is to develop a system that can detect and classify internal defects of agricultural produce on-line using VIS/NIR transmittance spectroscopy. And Shingo pear, which is one of the famous species of Korean pear, was used for the experiment. Soft independence modeling of class analogy (SIMCA) algorithm was employed to analyze the transmittance spectroscopic data qualitatively. On-line classification system was constructed and classification model was developed and validated. As a result, the correct classification rate (CCR) using the developed classification model was 96.1 %.

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A Study on Automatic Classification of Fingerprint Images (지문 영상의 자동 분류에 관한 연구)

  • Lim, In-Sic;Sin, Tae-Min;Park, Goo-Man;Lee, Byeong-Rae;Park, Kyu-Tae
    • Proceedings of the KIEE Conference
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    • 1988.07a
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    • pp.628-631
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    • 1988
  • This paper describes a fingerprint classification on the basis of feature points(whorl, core) and feature vector and uses a syntactic approach to identify the shape of flow line around the core. Fingerprint image is divided into 8 by 8 subregions and fingerprint region is separated from background. For each subregion of fingerprint region, the dominant ridge direction is obtained to use the slit window quantized in 8 direction and relaxation is performed to correct ridge direction code. Feature points(whorl, core, delta) are found from the ridge direction code. First classification procedure divides the types of fingerprint into 4 class based on whorl and cores. The shape of flow line around the core is obtained by tracing for the fingerprint which has one core or two core and is represented as string. If the string is acceptable by LR(1) parser, feature vector is obtained from feature points(whorl, core, delta) and the shape of flow line around the core. Feature vector is used hierarchically and linearly to classify fingerprint again. The experiment resulted in 97.3 percentages of sucessful classification for 71 fingerprint impressions.

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