A Method to Classify Shape Data using Multinomial Logistic Regression Model
DOI:
https://doi.org/10.19139/soic-2310-5070-2215Keywords:
Power-Divergence, Multinomial Logistic, Shape Analysis, Classification, Logistic RegressionAbstract
We introduced a multinomial logistic regression model to classify the labeled configurations. In this modeling, we use a power-divergence test to find an estimator for belonging probability in each category. The estimator is introduced based on different distances. Since the estimator is biased, we modified the belonging probability by multinomial logistic regression. We evaluate the performance of the proposed technique in the comprehensive simulation study. Also, we classified the five real data sets using our multinomial logistic model.Downloads
Published
2025-01-24
Issue
Section
Research Articles
License
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
How to Cite
A Method to Classify Shape Data using Multinomial Logistic Regression Model. (2025). Statistics, Optimization & Information Computing, 13(4), 1457-1471. https://doi.org/10.19139/soic-2310-5070-2215
