Systematic Literature Review of Mixed Variables Classification
Classification is one of the most popular approaches that had been used in a variety of fields. There are a lot of classification methods that are applicable to classify objects into their respective groups. Among the classification methods, the location model and smoothed location model received at...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
OJS/PKP
2023
|
| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/30819/1/IJDSAA%2005%2005%202023%20184-192.pdf https://repo.uum.edu.my/id/eprint/30819/ https://ijdsaa.com/index.php/welcome |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Utara Malaysia |
| Language: | English |
| Summary: | Classification is one of the most popular approaches that had been used in a variety of fields. There are a lot of classification methods that are applicable to classify objects into their respective groups. Among the classification methods, the location model and smoothed location model received attention when the data consists of mixed continuous and categorical variables. In this paper, classification, location model and smoothed location model are screened from the Scopus and Science Direct websites for review purposes. A total of 70 articles were chosen through the systematic review processes based on the related studies’ topics. The studies had been reviewed and discussed under the topic of classification, location model and smoothed location model in different data situations. The systematic literature review has many benefits compared to traditional literature review. The systematic review process is able to provide a defined review process and fundamental priorities that can manage research bias easily. Based on the reviews, smoothed location model outperforms the other classification techniques in terms of classification performance. However, there are not many articles that particularly discuss the application of the location model and smoothed location model in mixed variables classification. Therefore, smoothed location model is suggested to be considered in the future work for mixed variables classification situation |
|---|