• 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.

• Journal of IKEEE
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• v.24 no.4
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• pp.1180-1183
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• 2020

Recently, the daily life safety detection functionalities such as fall accident detection and burn danger detection are widely disseminated along with the development of IoT and smart home. These safety detection functionalities are mostly performed by artificial intelligence. However, simple accuracy measurement of the safety detection in laboratory environment is often far from practical performance in daily life environment. To mitigate this problem, this paper introduces two techniques, i.e. living lab and confusion matrix. Living lab is more than simple simulation of daily life environment, and it enables users to directly participate technology development and product design. Various performance measures induced from confusion matrix significantly help to evaluate the performance of artificial intelligence system for proper application purposes.

• Journal of the Ergonomics Society of Korea
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• v.19 no.1
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• pp.11-22
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• 2000

In this research report, we are presenting several optimization models for cognitive systems by using stimulus-response matrix (S-R Matrix). Stimulus-response matrices are widely used for tabulating results from various experiments and cognition systems design in which the recognition and confusability of stimuli. This paper is relevant to analyze the optimization/mathematical programming models. The weakness and restrictions of the existing models are resolved by generalization considering average confusion of each subset of stimuli. Also, clustering strategies are used in the extended model to obtain centers of cluster in terms of minimal confusion as well as the character of each cluster.

• Phonetics and Speech Sciences
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• v.5 no.1
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• pp.47-54
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• 2013

The aim of this study was to analyze consonant articulation errors produced by 90 speakers with cerebral palsy (CP). Phonetic transcriptions were made for 37 single-word utterances containing 70 phonemes: 48 initial consonants and 22 final consonants. Errors of substitution, omission, and distortion were analyzed using a confusion matrix paradigm showing the visualization of error patterns. Results showed that substitution errors in initial and final consonants were most frequent, followed by omission and distortion. Consonant omission occurred more frequently on final consonants. In both initial and final consonants, the within-place errors were more prominent than the within-manner errors. The current results suggest that consonant confusion matrices for dysarthric speech may provide useful information for evaluating speech intelligibility and developing automatic speech recognition system of adults with CP associated dysarthria.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.123-125
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• 2022

In this paper, we introduce a system applied to long short-term memory using Yolo-pose. Using Yolo-pose from image data, data divided into daily life and falls are extracted and applied to LSTM for learning. In order to prevent overfitting, training is performed 8 to 2 validation and is represented by a confusion matrix. The result of Yolo-pose recorded 100% of both sensitivity and specificity, confirming that daily life and falls were well distinguished.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.605-608
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• 2000

혼동 행렬 (confusion matrix)은 자극 또는 인식대상(데이터)에 대한 반응을 데이터화함으로써 인식대상(데이터)의 특성분석을 통하여 복잡한 시스템을 효율적으로 통제, 관리하기 위한 분석기법에 사용된다. 클러스터링은 인식 시스템을 위한 기법으로서 다양한 분야에서 널리 활용되고 있다. 본 연구에서는 혼동 행렬을 이용한 최적화 모델을 통하여 클러스터링(Clustering) 문제의 새로운 접근법을 제시한다. 최근 수리 계획 분야에서 클러스터링 분야에 대한 연구가 계속되고 있는데 그러한 수리 모델과 혼동 행렬을 접목하여 새로운 모델을 제시한다.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.310-312
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• 2016

OCR(Optical Character Recognition)의 오류를 줄이기 위해 본 논문에서는 교정 어휘 쌍의 혼동 행렬(confusion matrix)과 나이브 베이즈 분류기($na{\ddot{i}}ve$ Bayes classifier)를 이용한 철자 교정 시스템을 제안한다. 본 시스템에서는 철자 오류 중 한글에 대한 철자 오류만을 교정하였다. 실험에 사용된 말뭉치는 한국어 원시 말뭉치와 OCR 출력 말뭉치, OCR 정답 말뭉치이다. 한국어 원시 말뭉치로부터 자소 단위의 언어모델(language model)과 교정 후보 검색을 위한 접두사 말뭉치를 구축했고, OCR 출력 말뭉치와 OCR 정답 말뭉치로부터 교정 어휘 쌍을 추출하고, 자소 단위로 분해하여 혼동 행렬을 만들고, 이를 이용하여 오류 모델(error model)을 구축했다. 접두사 말뭉치를 이용해서 교정 후보를 찾고 나이브 베이즈 분류기를 통해 확률이 높은 교정 후보 n개를 제시하였다. 후보 n개 내에 정답 어절이 있다면 교정을 성공하였다고 판단했고, 그 결과 약 97.73%의 인식률을 가지는 OCR에서, 3개의 교정 후보를 제시하였을 때, 약 0.28% 향상된 98.01%의 인식률을 보였다. 이는 한글에 대한 오류를 교정했을 때이며, 향후 특수 문자와 숫자 등을 복합적으로 처리하여 교정을 시도한다면 더 나은 결과를 보여줄 것이라 기대한다.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.603-613
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• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.

• Journal of the Korea Safety Management & Science
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• v.15 no.2
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• pp.243-253
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• 2013

This research paper introduces the application and implementation of medical decision metrics that classifies medical decision-making into four different metrics using statistical diagnostic tools, such as confusion matrix, normal distribution, Bayesian prediction and Receiver Operating Curve(ROC). In this study, the metrics are developed based on cross-section study, cohort study and case-control study done by systematic literature review and reformulated the structure of type I error, type II error, confidence level and power of detection. The study proposed implementation strategies for 10 quality improvement activities via 14 medical decision metrics which consider specificity and sensitivity in terms of ${\alpha}$ and ${\beta}$. Examples of ROC implication are depicted in this paper with a useful guidelines to implement a continuous quality improvement, not only in a variable acceptance sampling in Quality Control(QC) but also in a supplier grading score chart in Supplier Chain Management(SCM) quality. This research paper is the first to apply and implement medical decision-making tools as quality improvement activities. These proposed models will help quality practitioners to enhance the process and product quality level.