Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data
During the seaweed’s drying process, a lot of drying parameters are involved. One of the problems in regression analysis is the impact of heterogeneity parameters. The seaweed data was collected using sensor-smart farming technology attached to the v-Groove Hybrid Solar Drier. The proposed method...
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| Format: | Thesis |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://eprints.usm.my/60406/1/IBIDOJA%20OLAYEMI%20JOSHUA%20-%20TESIS24.pdf |
| _version_ | 1811138810880196608 |
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| author | Joshua, Ibidoja Olayemi |
| author_facet | Joshua, Ibidoja Olayemi |
| author_sort | Joshua, Ibidoja Olayemi |
| collection | USM |
| description | During the seaweed’s drying process, a lot of drying parameters are involved.
One of the problems in regression analysis is the impact of heterogeneity parameters.
The seaweed data was collected using sensor-smart farming technology attached to the
v-Groove Hybrid Solar Drier. The proposed method used the variance inflation factor
to identify the heterogeneity parameters. To determine the 15, 25, 35, and 45 highranking
important parameters for the seaweed, models such as ridge, random forest,
support vector machine, bagging, boosting, LASSO, and elastic net are used before
heterogeneity, after heterogeneity, and for the modified model. To reduce the outliers,
robust regressions such as M Huber, M Hampel, M Bi Square, MM, and S estimators
are used. Before the heterogeneity parameters were excluded from the model, the
hybrid model of the ridge with the M Hampel estimator showed that better significant
results were obtained with 2.14% outliers. After the heterogeneity parameters were
excluded from the model, the support vector machine with the MM estimator showed
that better significant results were obtained with 2.09% outliers. For the modified
model, LASSO with M Bi square estimator showed that better significant results were
obtained with 1.31% outliers. For future studies, the impact of heterogeneity using a
hybrid model with imbalanced data or missing values can be investigated. Ensemble
machine learning algorithms such as stacking, XGBoost, and AdaBoost can be used. |
| first_indexed | 2024-09-25T03:56:07Z |
| format | Thesis |
| id | usm.eprints-60406 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-09-25T03:56:07Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | usm.eprints-604062024-04-22T08:24:52Z http://eprints.usm.my/60406/ Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data Joshua, Ibidoja Olayemi QA1-939 Mathematics During the seaweed’s drying process, a lot of drying parameters are involved. One of the problems in regression analysis is the impact of heterogeneity parameters. The seaweed data was collected using sensor-smart farming technology attached to the v-Groove Hybrid Solar Drier. The proposed method used the variance inflation factor to identify the heterogeneity parameters. To determine the 15, 25, 35, and 45 highranking important parameters for the seaweed, models such as ridge, random forest, support vector machine, bagging, boosting, LASSO, and elastic net are used before heterogeneity, after heterogeneity, and for the modified model. To reduce the outliers, robust regressions such as M Huber, M Hampel, M Bi Square, MM, and S estimators are used. Before the heterogeneity parameters were excluded from the model, the hybrid model of the ridge with the M Hampel estimator showed that better significant results were obtained with 2.14% outliers. After the heterogeneity parameters were excluded from the model, the support vector machine with the MM estimator showed that better significant results were obtained with 2.09% outliers. For the modified model, LASSO with M Bi square estimator showed that better significant results were obtained with 1.31% outliers. For future studies, the impact of heterogeneity using a hybrid model with imbalanced data or missing values can be investigated. Ensemble machine learning algorithms such as stacking, XGBoost, and AdaBoost can be used. 2023-09 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/60406/1/IBIDOJA%20OLAYEMI%20JOSHUA%20-%20TESIS24.pdf Joshua, Ibidoja Olayemi (2023) Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QA1-939 Mathematics Joshua, Ibidoja Olayemi Modification Of Regression Models To Solve Heterogeneity Problem Using Seaweed Drying Data |
| title | Modification Of Regression Models To
Solve Heterogeneity Problem Using
Seaweed Drying Data |
| title_full | Modification Of Regression Models To
Solve Heterogeneity Problem Using
Seaweed Drying Data |
| title_fullStr | Modification Of Regression Models To
Solve Heterogeneity Problem Using
Seaweed Drying Data |
| title_full_unstemmed | Modification Of Regression Models To
Solve Heterogeneity Problem Using
Seaweed Drying Data |
| title_short | Modification Of Regression Models To
Solve Heterogeneity Problem Using
Seaweed Drying Data |
| title_sort | modification of regression models to solve heterogeneity problem using seaweed drying data |
| topic | QA1-939 Mathematics |
| url | http://eprints.usm.my/60406/1/IBIDOJA%20OLAYEMI%20JOSHUA%20-%20TESIS24.pdf |
| work_keys_str_mv | AT joshuaibidojaolayemi modificationofregressionmodelstosolveheterogeneityproblemusingseaweeddryingdata |