• 한국멀티미디어학회논문지
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• 제18권3호
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• pp.323-329
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• 2015

In this paper we applied pattern recognition approach to detect audio forgery. Classification of the microphone types and models can help determining the authenticity of the recordings. Canonical correlation analysis was applied to extract feature for microphone classification. We utilized the linear dependence between two near-silence regions. To utilize the advantage of multi-feature based canonical correlation analysis, we selected three commonly used features to capture the temporal and spectral characteristics. Using three different microphones, we tested the usefulness of multi-feature based characteristics of canonical correlation analysis and compared the results with single feature based method. The performance of classification rate was carried out using the backpropagation neural network. Experimental results show the promise of canonical correlation features for microphone classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권5호
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• pp.1373-1387
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• 2012

Contextual advertising is an important revenue source for major service providers on the Web. Ads classification is one of main tasks in contextual advertising, and it is used to retrieve semantically relevant ads with respect to the content of web pages. However, it is difficult for traditional text classification methods to achieve satisfactory performance in ads classification due to scarce term features in ads. In this paper, we propose a novel ads classification method that handles the lack of term features for classifying ads with short text. The proposed method utilizes a vocabulary expansion technique using semantic associations among terms learned from large-scale search query logs. The evaluation results show that our methodology achieves 4.0% ~ 9.7% improvements in terms of the hierarchical f-measure over the baseline classifiers without vocabulary expansion.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권4호
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• pp.251-258
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• 2005

Automatic document classification may differ significantly according to the characteristics of documents that are subject to classification, as well as classifier's performance. This research identifies e-mail document's characteristics to apply a three-step preprocessing algorithm that can minimize e-mail document's atypical characteristics. In the first 5go, uncertain based sampling algorithm that used Mean Absolute Deviation(MAD), is used to address the question of selection learning document for the rule generation at the time of classification. In the subsequent stage, Weighted vlaue assigning method by attribute is applied to increase the discriminating capability of the terms that appear on the title on the e-mail document characteristic level. in the third and last stage, accuracy level during classification by each category is increased by using Naive Bayesian Presumptive Algorithm's Dynamic Threshold. And, we implemented an E-Mail Recommendtion System using a three-step preprocessing algorithm the enable users for direct and optimal classification with the recommendation of the applicable category when a mail arrives.

• Journal of information and communication convergence engineering
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• 제11권4호
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• pp.268-273
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• 2013

In this paper, we propose a document classification model using Web documents as a part of the training corpus in order to resolve the imbalance of the training corpus size per category. For the purpose of retrieving the Web documents closely related to each category, the proposed document classification model calculates the matching score between word features and each category, and generates a Web search query by combining the higher-ranked word features and the category title. Then, the proposed document classification model sends each combined query to the open application programming interface of the Web search engine, and receives the snippet results retrieved from the Web search engine. Finally, the proposed document classification model adds these snippet results as Web documents to the training corpus. Experimental results show that the method that considers the balance of the training corpus size per category exhibits better performance in some categories with small training sets.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회B
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• pp.2423-2428
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• 2008

A turbo classifier having a rotating rotor of two stage classification region has been developed to have a cut size of 1 micro meter. Particle number concentrations were counted using Aerosol Particle Sizer (APS, TSI co., USA) at inlet and outlet of the classifier. Partial classification efficiency was obtained at various rotation speeds, total flow rates, and feed rates of powders, and classification characteristic depending on design parameters was discussed. Classification performance was enhanced as rotation speed of rotor increased and total flow rate decreased.

• 반도체디스플레이기술학회지
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• 제10권3호
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• pp.75-81
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• 2011

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

• 대한산업공학회지
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• 제41권5호
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• pp.439-446
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• 2015

Recently, the importance of one-class classification problem is more increasing. However, most of existing algorithms have the limitation on providing the information that effects on the prediction of the target value. Motivated by this remark, in this paper, we suggest an efficient one-class classifier using hyper-rectangles (H-RTGLs) that can be produced from intervals including observations. Specifically, we generate intervals for each feature and integrate them. For generating intervals, we consider two approaches : (i) interval merging and (ii) clustering. We evaluate the performance of the suggested methods by computing classification accuracy using area under the roc curve and compare them with other one-class classification algorithms using four datasets from UCI repository. Since H-RTGLs constructed for a given data set enable classification factors to be visible, we can discern which features effect on the classification result and extract patterns that a data set originally has.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 추계정기학술대회:지능형기술과 CRM
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• 한국멀티미디어학회논문지
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• 제24권10호
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• pp.1358-1368
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• 2021

A research on agricultural automation is a main issues to overcome the shortage of labor in Korea. A sweet persimmon farmers need much time and labors for classifying profitable sweet persimmon and ill profitable products. In this paper, we propose a mixed two-step synthetic neural network model for efficiently classifying sweet persimmon images. In this model, we suggested a surface direction classification model and a quality screening model which constructed from image data sets. Also we studied Class Activation Mapping(CAM) for visualization to easily inspect the quality of the classified products. The proposed mixed two-step model showed high performance compared to the simple binary classification model and the multi-class classification model, and it was possible to easily identify the weak parts of the classification in a dataset.

• Communications for Statistical Applications and Methods
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• 제29권5호
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• pp.603-614
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• 2022

The naive Bayes classifier is one of the most straightforward classification tools and directly estimates the class probability. However, because it relies on the independent assumption of the predictor, which is rarely satisfied in real-world problems, its application is limited in practice. In this article, we propose employing sufficient dimension reduction (SDR) to substantially improve the performance of the naive Bayes classifier, which is often deteriorated when the number of predictors is not restrictively small. This is not surprising as SDR reduces the predictor dimension without sacrificing classification information, and predictors in the reduced space are constructed to be uncorrelated. Therefore, SDR leads the naive Bayes to no longer be naive. We applied the proposed naive Bayes classifier after SDR to build a recommendation system for the eyewear-frames based on customers' face shape, demonstrating its utility in the top-k classification problem.