• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.161-169
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• 2021

The two-dimensional confusion matrix used in credit assessment, biostatistics, and many other fields consists of true positive, true negative, false positive, and false negative. Their rates, such as the true positive rate (TPR), true negative rate (TNR), false positive rate, and false negative rate, can be applied to measure its accuracy. In this study, we propose the TPR-TNR plot, a graphical method that can geometrically describe and explain these rates based on the confusion matrix. The proposed TPR-TNR plot consists of two right-angled triangles. We obtain that the TPR and TNR describe the acute angles of right-angled triangles in the plot. These acute angles can be used to determine optimal thresholds corresponding to lots of accuracy measures.

• ETRI Journal
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• v.27 no.4
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• pp.461-464
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• 2005

Recently, a variety of confidence measures for utterance verification has been studied to improve speech recognition performance by rejecting out-of-vocabulary inputs. Most of the conventional confidence measures for utterance verification are based primarily on hypothesis testing or an approximated posterior probability, and their performances depend on the robustness of an alternative hypothesis or the prior probability. We introduce a novel confidence measure called a search confusion rate (SCR), which does not require an alternative hypothesis or the approximation of posterior probability. Our confusion-based approach shows better performance in additive noise-corrupted speech as well as in clean speech.

• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.1001-1010
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• 2007

HMM tends to output high probability for not only the proper class data but confusable class data, since the modeling power increases as the number of parameters increases. Thus it may not be helpful for discrimination to simply increase the number of parameters of HMM. We proposed two methods in this paper. One is a CMC(Confusion Likelihood Model Selection Criterion) using confusion class data probability, the other is a new recognition method, RCM(Recognition Using Confusion Models). In the proposed recognition method, confusion models are constructed using confusable class data, then confusion models are used to depress misrecognition by confusion likelihood is subtracted from the corresponding standard model probability. We found that CMC showed better results using fewer number of parameters compared with ML, ALC2, and BIC. RCM recorded 93.08% recognition rate, which is 1.5% higher result by reducing 17.4% of errors than using standard model only.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.107-109
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• 2003

Optical sensor-based land cover categories produce spectral signature confusion along with degraded classification accuracy. In the classification tasks, the goal of fusing data from different sensors is to reduce the classification error rate obtained by single source classification. This paper describes the result of land cover/land use classification derived from solely of Landsat TM (TM) and multisensor image fusion between JERS 1 SAR (JERS) and TM data. The best radar data manipulation is fused with TM through various techniques. Classification results are relatively good. The highest Kappa Coefficient is derived from classification using principal component analysis-high pass filtering (PCA+HPF) technique with the Overall Accuracy significantly high.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.1-9
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• 2021

Deep learning in computer vision has made accelerated improvement over a short period but large-scale learning data and computing power are still essential that required time-consuming trial and error tasks are involved to derive an optimal network model. In this study, we propose a similar image classification performance improvement method based on CR (Confusion Rate) that considers only the characteristics of the data itself regardless of network optimization or data reinforcement. The proposed method is a technique that improves the performance of the deep learning model by calculating the CRs for images in a dataset with similar characteristics and reflecting it in the weight of the Loss Function. Also, the CR-based recognition method is advantageous for image identification with high similarity because it enables image recognition in consideration of similarity between classes. As a result of applying the proposed method to the Resnet18 model, it showed a performance improvement of 0.22% in HanDB and 3.38% in Animal-10N. The proposed method is expected to be the basis for artificial intelligence research using noisy labeled data accompanying large-scale learning data.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.8
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• pp.761-765
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• 2010

This paper presents the design of optimal fuzzy classifier for human detection by using genetic algorithms, one of the best-known meta-heuristic search methods. For this purpose, encoding scheme to search the optimal sequential intersection points between adjacent fuzzy membership functions is originally presented for the fuzzy classifier design for HOG (Histograms of Oriented Gradient) descriptors. The intersection points are sequentially encoded in the proposed encoding scheme to reduce the redundancy of search space occurred in the combinational problem. Furthermore, the fitness function is modified with the true-positive and true-negative of the confusion matrix instead of the total success rate. Experimental results show that the two proposed approaches give superior performance in HOG datasets.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.554-559
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• 2009

This paper proposes a new method of weighted template matching fur machine-printed numeral recognition. The proposed weighted template matching, which emphasizes the feature of a pattern using adaptive Hamming distance on local feature areas, improves the recognition rate while template matching processes an input image as one global feature. The experiment compares confusion matrices of the template matching, error back propagation neural network classifier, and the proposed weighted template matching respectively. The result shows that the proposed method improves fairly the recognition rate of the machine-printed numerals.

• The Korean Journal of Pain
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• v.27 no.4
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• pp.360-364
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• 2014

The causes of sciatica are variable and include musculoskeletal, dermatologic, infectious, neoplastic, and vascular disorders. In many cases, the symptom is usually caused by degenerative disease in the spine with the compression or irritation of spinal nerve. On the other hands, there are also several announced extra-spinal causes including aneurysm, diabetes, and radiation for sciatica in a low rate. Among the extra-spinal cases, aneurysms arising from iliac vessels are sometimes developing a diagnostic confusion with the spinal causes, and delayed diagnosis can lead to poor prognosis. It is very important to pay attention weather the aneurysmal cause is involved in the symptom of sciatica.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.10
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• pp.496-499
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• 2005

In many parts of the world, the electric power industry is undergoing unprecedented changes. Therefore, in order to reform the electric power industry efficiently and minimize the confusion of this restructuring, the systematic studies related to transmission pricing and transmission cost allocation issues are required essentially. However, even now, the basis of transmission cost allocation rate is not equipped so that the regulation body has determined the allocating rate under the common practice. In this paper, we demonstrate that the decision of transmission cost allocation rate is the regulation body's own right. For this analysis, we apply game theory to the procedure determining this rate and the competition to determine this rate between gencos and distcos is modeled as the arbitration game.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.375-377
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• 2002

In many parts of the world. the electricity industry is undergoing unprecedented changes. Hence, in order to reform the electricity industry readily and efficiently and minimize the confusion by these restructuring, it is required the systematic studies related to transmission pricing and transmission cost allocation issues. However, even now the basis of transmission cost allocation rate is not equipped so that the regulation body has determined the allocating rate under the common practice. In this paper it is demonstrated that the decision of transmission cost allocation rate is the regulation body's own right. For the analysis, game theory is applied to the procedure determining this rate and the competition to determine this rate between generators and distributors is modeled as the arbitration game.