• Title, Summary, Keyword: discriminant analysis

Search Result 1,378, Processing Time 0.057 seconds

A Study of Discriminant Analysis about Korean Quick Response System Adoption (국내(國內) 신속대응(迅速對應)시스템 도입업체(導入業體)의 판별분석(判別分析) 연구(硏究))

  • Ko, Eun-Ju
    • Journal of Fashion Business
    • /
    • v.4 no.3
    • /
    • pp.103-114
    • /
    • 2000
  • The purpose of this study was to test the discriminant analysis model of Quick Response system and to examine the detailed relationship between each discriminant factor and Quick Response adoption. In this discriminant analysis model of Quick Response system, firm size, strategic type, product category, fashion trend, selling time and the Quick Response benefits were included as discriminant factors. Onehundred and two subjects were randomly selected for the survey study and discriminant analysis, descriptive analysis, t-test, and x square test were used for the data analysis. The results of this study were: 1. Wilks Lambda and F value support the discriminant analysis model that, taken together firm size, strategic type, product category, fashion trend, selling time and the Quick Response benefits significantly help to explain Quick Response adoption. 2. The importance of discriminant ability was, in order, firm size, the Quick Response benefits, women's wear, fashion trend, analyzer, selling time, reactor, defender and men's wear. 3. The discriminant function had the high hit ratio, so this can be well used for the classification of Quick Response adoption/nonadoption.

  • PDF

Discrimination between earthquake and explosion by using seismic spectral characteristics and linear discriminant analysis (지진파 스펙트럼특성과 선형판별분석을 이용한 자연지진과 인공지진 식별)

  • 제일영;전정수;이희일
    • Proceedings of the Earthquake Engineering Society of Korea Conference
    • /
    • /
    • pp.13-19
    • /
    • 2003
  • Discriminant method using seismic signal was studied for discrimination of surface explosion. By means of the seismic spectral characteristics, multi-variate discriminant analysis was performed. Four single discriminant techniques - Pg/Lg, Lg1/Lg2, Pg1/Pg2, and Rg/Lg - based on seismic source theory were applied to explosion and earthquake training data sets. The Pg/Lg discriminant technique was most effective among the four techniques. Nevertheless, it could not perfectly discriminate the samples of the training data sets. In this study, a compound linear discriminant analysis was defined by using common characteristics of the training data sets for the single discriminants. The compound linear discriminant analysis was used for the single discriminant as an independent variable. From this analysis, all the samples of the training data sets were correctly discriminated, and the probability of misclassification was lowered to 0.7%.

  • PDF

DISCRIMINANT ANALYSIS OF LOGICAL RELATIONS

  • Osawa, Mitsuru
    • Proceedings of the Korean Society for Emotion and Sensibility Conference
    • /
    • /
    • pp.157-162
    • /
    • 2000
  • Discriminant analysis is a method to relate whether the objects have a specific characteristic or not with their 'continuous' attribute values and, for given objects, to estimate whether they have a specific characteristic or not by their values of discriminant scores gotten from their attribute values. The author developed the new 'computational' method of discriminant analysis without specific hypotheses or assumptions and, by this new method, we can find 'feasible' solutions under the conditions required by our actual problems. In this paper, the author tried to apply this new method to the discrimination of logical relations. If this trial could be a success, we can apply this new method of discriminant analysis to the problems about relating the specific characteristic of the objects with their 'discrete' attribute values. The result was successful and the applicability of discriminant analysis could be expanded as a method for constructing the models for "estimating impressions".

  • PDF

A Kernel Approach to Discriminant Analysis for Binary Classification

  • Shin, Yang-Kyu
    • Journal of the Korean Data and Information Science Society
    • /
    • v.12 no.2
    • /
    • pp.83-93
    • /
    • 2001
  • We investigate a kernel approach to discriminant analysis for binary classification as a machine learning point of view. Our view of the kernel approach follows support vector method which is one of the most promising techniques in the area of machine learning. As usual discriminant analysis, the kernel method can discriminate an object most likely belongs to. Moreover, it has some advantage over discriminant analysis such as data compression and computing time.

  • PDF

Design of Pattern Classification Rule based on Local Linear Discriminant Analysis Classifier by using Differential Evolutionary Algorithm (차분진화 알고리즘을 이용한 지역 Linear Discriminant Analysis Classifier 기반 패턴 분류 규칙 설계)

  • Roh, Seok-Beom;Hwang, Eun-Jin;Ahn, Tae-Chon
    • Journal of Korean Institute of Intelligent Systems
    • /
    • v.22 no.1
    • /
    • pp.81-86
    • /
    • 2012
  • In this paper, we proposed a new design methodology of a pattern classification rule based on the local linear discriminant analysis expanded from the generic linear discriminant analysis which is used in the local area divided from the whole input space. There are two ways such as k-Means clustering method and the differential evolutionary algorithm to partition the whole input space into the several local areas. K-Means clustering method is the one of the unsupervised clustering methods and the differential evolutionary algorithm is the one of the optimization algorithms. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods.

Applicability of Cluster Analysis and Discriminant Analysis (집락분석과 판별분석의 활용성연구)

  • Chae, Seong-San;Hwang, Jung-Yeon
    • Journal of the Korean Society for Quality Management
    • /
    • v.22 no.2
    • /
    • pp.143-153
    • /
    • 1994
  • Cluster analysis is a primitive technique in which no assumptions are made concerning the data structure. And the number of groups is known a priori discriminant analysis provides an information how well N individuals are classified into their own groups. In this study, clustering, which is any partition of a collection of data points, generated by the application of eight hierarchical clustering methods was re-classified by discriminant analysis. Then correct classification ratios were obtained for the application of discriminant analysis through each clustering method and the direct application of discriminant analysis. By comparing the correct classification ratios, the applicability of cluster analysis and discriminant analysis considered.

  • PDF

Results of Discriminant Analysis with Respect to Cluster Analyses Under Dimensional Reduction

  • Chae, Seong-San
    • Communications for Statistical Applications and Methods
    • /
    • v.9 no.2
    • /
    • pp.543-553
    • /
    • 2002
  • Principal component analysis is applied to reduce p-dimensions into q-dimensions ( $q {\leq} p$). Any partition of a collection of data points with p and q variables generated by the application of six hierarchical clustering methods is re-classified by discriminant analysis. From the application of discriminant analysis through each hierarchical clustering method, correct classification ratios are obtained. The results illustrate which method is more reasonable in exploratory data analysis.

Evaluating Distress Prediction Models for Food Service Franchise Industry (외식프랜차이즈기업 부실예측모형 예측력 평가)

  • KIM, Si-Joong
    • The Journal of Distribution Science
    • /
    • v.17 no.11
    • /
    • pp.73-79
    • /
    • 2019
  • Purpose: The purpose of this study was evaluated to compare the predictive power of distress prediction models by using discriminant analysis method and logit analysis method for food service franchise industry in Korea. Research design, data and methodology: Forty-six food service franchise industry with high sales volume in the 2017 were selected as the sample food service franchise industry for analysis. The fourteen financial ratios for analysis were calculated from the data in the 2017 statement of financial position and income statement of forty-six food service franchise industry in Korea. The fourteen financial ratios were used as sample data and analyzed by t-test. As a result seven statistically significant independent variables were chosen. The analysis method of the distress prediction model was performed by logit analysis and multiple discriminant analysis. Results: The difference between the average value of fourteen financial ratios of forty-six food service franchise industry was tested through t-test in order to extract variables that are classified as top-leveled and failure food service franchise industry among the financial ratios. As a result of the univariate test appears that the variables which differentiate the top-leveled food service franchise industry to failure food service industry are income to stockholders' equity, operating income to sales, current ratio, net income to assets, cash flows from operating activities, growth rate of operating income, and total assets turnover. The statistical significances of the seven financial ratio independent variables were also confirmed by logit analysis and discriminant analysis. Conclusions: The analysis results of the prediction accuracy of each distress prediction model in this study showed that the forecast accuracy of the prediction model by the discriminant analysis method was 84.8% and 89.1% by the logit analysis method, indicating that the logit analysis method has higher distress predictability than the discriminant analysis method. Comparing the previous distress prediction capability, which ranges from 75% to 85% by discriminant analysis and logit analysis, this study's prediction capacity, which is 84.8% in the discriminant analysis, and 89.1% in logit analysis, is found to belong to the range of previous study's prediction capacity range and is considered high number.

A Comparison of the Discrimination of Business Failure Prediction Models (부실기업예측모형의 판별력 비교)

  • 최태성;김형기;김성호
    • Journal of the Korean Operations Research and Management Science Society
    • /
    • v.27 no.2
    • /
    • pp.1-13
    • /
    • 2002
  • In this paper, we compares the business failure prediction accuracy among Linear Programming Discriminant Analysis(LPDA) model, Multivariate Discriminant Analysis (MDA) model and logit analysis model. The Data for 417 companies analyzed were gathered from KIS-FAS Published by Korea Information Service in 1999. The result of comparison for four time horizons shows that LPDA Is advantageous in prediction accuracy over the other two models when over all tilt ratio and business failure accuracy are considered simultaneously.

Derivation and Application of In uence Function in Discriminant Analysis for Three Groups (세 집단 판별분석 상황에서의 영향함수 유도 및 그 응용)

  • Lee, Hae-Jung;Kim, Hong-Gie
    • The Korean Journal of Applied Statistics
    • /
    • v.24 no.5
    • /
    • pp.941-949
    • /
    • 2011
  • The influence function is used to develop criteria to detect outliers in discriminant analysis. We derive the influence function of observations that estimate the the misclassification probability in discriminant analysis for three groups. The proposed measures are applied to the facial image data to define outliers and redo the discriminant analysis excluding the outliers. The study proves that the derived influence function is more efficient than using the discriminant probability approach.