• Journal of Digestive Cancer Research
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• v.5 no.1
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• pp.5-15
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• 2017

Hepatocellular carcinoma (HCC) is the sixth most common cancer and second leading cause of cancer-related death in the world. The aggressive but not always predictable pattern of HCC causes the limited treatment option and poorer outcome. Many researches had already proven the heterogeneity of HCC is one of the major challenges for treatment option and prognosis prediction. Molecular subtyping of HCC and selection of patient based on molecular profile can provide the optimization in the treatment and prognosis prediction. In this review, we have tried to summarize the molecular classification of HCC proposed by different valuable researches presented in the logistic way.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.652-662
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• 2023

Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.35-46
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• 2011

In this paper, we proposed the method of emotion recognition using staged classification model and Fisher's linear discriminant. By organizing the staged classification model, the proposed method improves the classification rate on the Fisher's feature space with high complexity. The staged classification model is achieved by the successive combining of binary classification model which has simple structure and high performance. On each stage, it forms Fisher's linear discriminant according to the two groups which contain each emotion class, and generates the binary classification model by using Adaboost method on the Fisher's space. Whole learning process is repeatedly performed until all the separations of emotion classes are finished. In experimental results, the proposed method provides about 72% classification rate on 8 classes of emotion and about 93% classification rate on specific 3 classes of emotion.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.718-723
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• 2008

Bearing is one of the important mechanical elements used in various industrial equipments. Most of failures occurred during the equipment operation result from bearing defects and breakages. Therefore, monitoring of bearings is essential in preventing equipment breakdowns and reducing unexpected loss. The purpose of this paper is to present an online monitoring method to predict bearing states using vibration signals. Bearing vibrations, which are collected as a form of profile signal, are first analyzed by a discrete wavelet transform. Next, some statistical features are obtained from the resultant wavelet coefficients. In order to select significant ones among them, analysis of variance (ANOVA) is employed in this paper. Statistical features screened in this way are used as input variables to support vector machine (SVM). An hierarchical SVM tree is proposed for dealing with multi-class problems. The result of numerical experiments shows that the proposed SVM tree has a competent performance for classifying bearing fault states.

• Journal of the Korean Society for Library and Information Science
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• v.45 no.4
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• pp.77-102
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• 2011

The purpose of this study is to design, implement and evaluate a personalized search system using gathered information on users to provide more accurate search results. For this purpose, a hybrid-based user profile is constructed by using subject classification. In order to evaluate the performance of the proposed system, experts directly measured and evaluated MRR, MAP and usability by using the Korean journal articles of science and technology DB. Its performance was better than the general search system in the area of "Computer Science" and "Library and Information Science". Especially better results were shown when tested on ambiguous keywords. Evaluation through in-depth interviews proved that the proposed personalized search system was more efficient in looking up and obtaining information. In addition, the proposed personalized search system provided a variety of recommendation systems which proved helpful in navigating for new information. High user satisfaction ratings on the proposed personalized search system were another proof of its usefulness. In this study, we were able to prove through expert evaluation that the proposed personalized search system was more efficient in information retrieval.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.482-491
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• 2022

The purpose of this study was to classify latent profiles based on satisfaction of student by the campus-life&educational-experiences and to identify factors affecting satisfaction according to each type. For this study, data from the survey of the A univ(1,952 data) were used. To analyze this, a latent profiles analysis was applied to identify subgroups, in which the students by the campus-life&educational-experiences satisfaction, and a multinomial logistic regression model was applied to verify factors affecting group classification. As a result of the analysis, first four groups were classified in the order of 'average·class·highest·relationship satisfaction type'. Second the factors affecting the classification into the remaining three types with 'the average satisfaction type' as a reference group were found to be significant influencing factors(gender, grade, admission process, GPA grade). Based on these results, this study suggested implications for planning and promoting student-tailored education and student support policies at the university level.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.5
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• pp.47-52
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• 2011

Vehicle classification data are considerably used in the almost all fields of transportation planning and engineering. Highway agencies use a large number of vehicle classification schemes. Vehicles on the national highway are classified by 12-Category classification system, using number of axles, distances between axles, vehicle length, overhang, and other factors. In the case of using existing axle-sensor-based classification counters (that is, 12-category classification system), two-axle vehicles(Class 1 to 4) can be erroneously classified because a passenger vehicle becomes larger and similar with class 3 and 4. In this reason, this study proposes the vehicle classification scheme based on using vehicle height profiles obtained by a laser sensors. Also, the accuracy of the proposed method are tested through a field study.

• Journal of Microbiology and Biotechnology
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• v.23 no.7
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• pp.932-941
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• 2013

This work aimed to classify Aspergillus (8 species, 28 strains) by using a secondary metabolite profile-based chemotaxonomic classification technique. Secondary metabolites were analyzed by liquid chromatography ion-trap mass spectrometry (LC-IT-MS) and multivariate statistical analysis. Most strains were generally well separated from each section. A. lentulus was discriminated from the other seven species (A. fumigatus, A. fennelliae, A. niger, A. kawachii, A. flavus, A. oryzae, and A. sojae) with partial least-squares discriminate analysis (PLS-DA) with five discriminate metabolites, including 4,6-dihydroxymellein, fumigatin, 5,8-dihydroxy-9-octadecenoic acid, cyclopiazonic acid, and neosartorin. Among them, neosartorin was identified as an A. lentulus-specific compound that showed anticancer activity, as well as antibacterial effects on Staphylococcus epidermidis. This study showed that metabolite-based chemotaxonomic classification is an effective tool for the classification of Aspergillus spp. with species-specific activity.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.175-184
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• 2008

To construct user profiles automatically, an extraction method for representative keywords from a set of documents is needed. In our previous works, we suggested such a method and showed its usefulness. Here, we apply it to the classification problem and observe how much it contributes to performance improvement. The method can be used as a linear document classifier with few modifications. So, we first evaluate its performance for that case. The method is also applicable to some non-linear classification methods such as GIS (Generalized Instance Set). In GIS algorithm, generalized instances are built from training documents by a generalization function and then the K-NN algorithm is applied to them, where the method can be used as a generalization function. For comparative works, two famous linear classification methods, Rocchio and Widrow-Hoff algorithms, are also used. Experimental results show that our method is better than the others for the case that only positive documents are considered, but not when negative documents are considered together.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.76-87
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• 2005

In this paper, we propose a new profile, ROHC(RObust Header Compression) profile 7, for SCTP with ROHC for applying robust header compression SCTP, which is a transport layer protocol. The proposed new profile 7 adds a new field of 1 or 2 byte size on the existing SCTP packet header, which can make the SCTP stream to be diveded into acknowledgement stream and data stream. In addition, the classification of the stream can be used for recovering fault context. Consequently, in the case of using proposed ROHC-SCTP, it is possible to reduce the SCTP header overhead rate and also can be saved bandwidth.