• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.30 no.1
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• pp.9-16
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• 2019

Objectives: The purpose of this study was to analyze the discriminant validity and the clinical cut off scores of the Child Behavior Checklist 1.5-5 (CBCL 1.5-5) in the diagnosis of autism spectrum disorder (ASD) and non-ASD. Methods: In total, 104 ASD and 441 non-ASD infants were included in the study. T-test, discriminant analysis, receiver operating characteristic (ROC) curve analysis, and odds ratio analysis were performed on the data. Results: The discriminant validity was confirmed by mean differences and discriminant analysis on the subscales of Emotionally reactive, Somatic complaints, Withdrawn, Sleep problems, Attention problems, Aggressive behavior, Internalizing problems, Externalizing problems, and Total problems, along with the Diagnostic and Statistical Manual of Mental Disorders (DSM)-oriented scales between the two groups. ROC analysis showed that the following subscales significantly separated ASD from normal infants: Emotionally reactive, Somatic complaints, Withdrawn, Sleep problems, Attention problems, Aggressive behavior, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems. Moreover, the clinical cut off score criteria adopted in the Korean-CBCL 1.5-5 were shown to be valid for the subscales Withdrawn, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems. Conclusion: The subscales of Withdrawn, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems significantly discriminated infants with ASD.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2853-2864
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• 2018

Discriminant analysis predicts a class label of a new observation with an unknown label, using information from the existing labeled data. Hence, observed labels play a critical role in the analysis and we usually assume that these labels are correct. If the observed label contains an error, the data has label noise. Label noise can frequently occur in real data, which would affect classification performance. In order to resolve this, a comparative study was carried out using simulated data with label noise. In particular, we considered 4 different classification techniques such as LDA (linear discriminant analysis classifiers), QDA (quadratic discriminant analysis classifiers), KNN (k-nearest neighbour), and SVM (support vector machine). Then we evaluated each method via average accuracy using generated data from various scenarios. The effect of label noise was investigated through its occurrence rate and type (noise location). We confirmed that the label noise is a significant factor influencing the classification performance.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.43-52
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• 1999

The local influence method is adapted to quadratic discriminant analysis for the identification of influential observations affecting the estimation of probability density function probabilities and log odds. The method allows a simultaneous perturbation on all observations so that it can identify multiple influential observations. The proposed method is applied to a real data set and satisfactory result is obtained.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.320-329
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• 1995

The purpose of this paper is to simplify and study the asymptotic distribution of the dimensionality estimators in discriminant analysis based on Akaike's and Mallows' methods for samples from a multivariate distribution with finite fourth moments.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.5
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• pp.573-581
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• 2002

This study was done to classify children's somatotypes and to provide the fundamental data or their clothing sizing system for the purpose of designing patterns fur children's wear and standardizing sizes of ready-made clothes. The sampling was done for 7-8 years-old-girl living in Pusan and Kyungsangnam-do. Data from each girl comprises 33 anthropometic measurments and 7 photogrphic measurments, based on the somatometric characteristics of girls which I had obtained. Factor analysis, cluster analysis, discriminant analysis were performed for statistical analysis of the data. Seven factors which explain 76.49% of the whole variances were extracted. The thirst and second factors which explain more than 70% of the whole variances represent 'horizontal size 'and 'vortical size', which characterize most aspects of the body shape of the subjects. On the basis of the cluster analysis, three different upper body types were categorized. Type 1 has quite long surface length of the upper body and rising shoulders and are close to the averages of this age group. Type 2 has highest stature, biggest frame, dropped shoulders and surface length of the upper body similar to the type 1. Type 3 has shortest stature, smallest frame and sloping shoulders. According to the analysis to discriminate somatotypes of the upper body by this age group, the discriminative items in discriminant function are follows. As this group, waist circumference of discriminant function 1 and front length and length between both shoulder points of discriminant function 2 have large coefficient values.

• Current Research on Agriculture and Life Sciences
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• v.4
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• pp.154-162
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• 1986

This paper is studied in order to examine eight provinces as Agricultural zone in the early Chosun-Dynasty. The research method of this study depends on discriminant analysis. The research data is obtained from geography of Sejong sillok. In this approach, we believe the possibility of more scientific analysis of agricultural zone. So all cross-sectional data of this period were analysed by means of the discriminant analysis method. In the discriminant analysis of the eight provinces, 54.6% of the country were discriminated. In the territorial map, Kyongsang and Cholla were crossly adjoined and Chungcheong was located in the middle of them. While, Kyonggky and Hwanghae were adjoined in the center, and Pyongan and Hamgil were crossly adjoined, too. And Kangwon was located in the middle of them. Consequently their regional distribution varied widely and the agriculture of this period had considerable regional gaps.

• Journal of Korean Tunnelling and Underground Space Association
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• v.6 no.3
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• pp.237-245
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• 2004

In designing a tunnel, RMR has been widely used to classify rock mass and to decide the support pattern according to the class of rock mass. However, this RMS system can't help relying on the empirical judgment of engineers who use variables which can be obtained only through consideration of the site conditions. In actuality, it is impossible to consider all the rating factors of RMS when using RMR system at the stage of designing. Therefore, in order to confirm possibility of RMR by use of only the quantitative factors for designing, this paper has done discriminant analysis. Rock strength or RQD has high coefficient of correlation with RMR value, and in consideration of the existing standards for rock mass classification, rock intensity and RQD are important factors for classification of rock mass. Through rock mass classification by the existing RMR system and rock mass classification by the discriminant analysis which has considered two variables only, the discriminant analysis using the rock intensity as an independent variable has shown 74.8% accuracy while the discriminant analysis using RQD as an independent variable has shown 74.3% accuracy. In case of the discriminant analysis which has considered both rock intensity and RQD, it has shown 82.5% accuracy. The existing cases have shown 40.3% accuracy at the stage of designing in which all the RMR factors are considered. It means that at the stage of designing, RMR system can work only with the rock intensity and RQD.

• Geomechanics and Engineering
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• v.13 no.5
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• pp.861-879
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• 2017

Coal and gas outburst is a serious dynamic disaster that occurs during coal mining and threatens the lives of coal miners. Currently, coal and gas outburst is commonly predicted using single indicator and its critical value. However, single indicator is unable to fully reflect all of the factors impacting outburst risk and has poor prediction accuracy. Therefore, a more accurate prediction method is necessary. In this work, we first analyzed on-site impacting factors and precursors of coal and gas outburst; then, we constructed a Fisher discriminant analysis (FDA) index system using the gas adsorption index of drilling cutting ${\Delta}h_2$, the drilling cutting weight S, the initial velocity of gas emission from borehole q, the thickness of soft coal h, and the maximum ratio of post-blasting gas emission peak to pre-blasting gas emission $B_{max}$; finally, we studied an FDA-based multiple indicators discriminant model of coal and gas outburst, and applied the discriminant model to predict coal and gas outburst. The results showed that the discriminant model has 100% prediction accuracy, even when some conventional indexes are lower than the warning criteria. The FDA method has a broad application prospects in coal and gas outburst prediction.

• International Journal of Human Ecology
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• v.16 no.2
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• pp.21-31
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• 2015

This study explored determinants of family support that young renter households received to afford their housing costs. Microdata set of the 2014 Korea Housing Survey was used as secondary data for the study. Total 1,752,899 households headed by persons between 20 and 34 years of age and whose rental type was either Jeon-se or monthly rental with deposit in private rental units were selected as study subjects. For the data analysis, a series of discriminant analysis was conducted using IBM SPSS 21.0. Major findings were as follows. (1) Among the subjects, 28.2% were found to receive financial support from parents or other relatives. (2) To see the discriminant analysis results, a linear combination of seven household and housing characteristics (householder's gender, whether or not the householder worked in the previous week, whether or not the householders have a spouse, tenure type, structure type, location and deposit amount) could explain 44.6% of variance in young renter households' receipt of family support with a prediction accuracy of 77.2%. (3) To summarize the final discriminant model, Jeon-se renter households in location other than Incheon or Gyeonggi Province living in a unit in structure other than multifamily structure headed by younger householders that did not worked previous week or without spouse; with a greater deposit had the maximum tendency to receive family support to pay rental costs.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.250-255
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• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.