Authors :
Habibu Aminu; Badamasi Imam Yau; Fatima Umar Zambuk; Emanuel Ramsom Nanin; Abdulmutallib Abdullahi; Ismail Zahraddeen Yakubu
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/5ehpe26e
Scribd :
http://tinyurl.com/2p9t5nj2
DOI :
https://doi.org/10.5281/zenodo.10629245
Abstract :
Machine learning implementations are
growing in every organization for forecasting their
employee’s working nature and competence by
calculating the time taken by the employee to complete
the task. Numerous recent research have been
published to evaluate the effectiveness of various
clssification algorithms for predicting the employee
salary classes. From the machine learning perception,
Salary prediction is a difficult task due to the small
sample size, relatively high dimensionality, and
presence of noise. To address this, to find more useful
features, deeper architectures are required.
Additionally, more data analysis and data processing
can be pragmatic to make the prediction model go
beyond the correlation and precision standards by
feature extraction techniques. Hence, this study
proposes an enhanced method for salary prediction that
selects a subset of characteristics from all available data
using a PCA system and a deep neural network (DNN)
model for the classification process. Upon assessment
with other classical machine learning methods such as
DT and RF. Better classification accuracy, precision,
recall, and F-score are achieved by the proposed DNN
model. Furthermore, the proposed DNN model achieves
the highest MAE of 94.9% as compared to DT and RF,
which attain an MAE score of 89.6% and 76.4%
respectively. This result suggests that the proposed
model has a prediction error of 5.1% which is fewer
when compared to DT and RF which has prediction
error of as much as 10.4% and 23.6%, thereby,
signifying the dominance of deep learning algorithm
over conventional machine learning algorithms in
salary classification and prediction task.
Keywords :
Principal Component Analysis, Random Forest, Deep Neural Network, Machine Learning, Deep Learning, and Decision Tree.
Machine learning implementations are
growing in every organization for forecasting their
employee’s working nature and competence by
calculating the time taken by the employee to complete
the task. Numerous recent research have been
published to evaluate the effectiveness of various
clssification algorithms for predicting the employee
salary classes. From the machine learning perception,
Salary prediction is a difficult task due to the small
sample size, relatively high dimensionality, and
presence of noise. To address this, to find more useful
features, deeper architectures are required.
Additionally, more data analysis and data processing
can be pragmatic to make the prediction model go
beyond the correlation and precision standards by
feature extraction techniques. Hence, this study
proposes an enhanced method for salary prediction that
selects a subset of characteristics from all available data
using a PCA system and a deep neural network (DNN)
model for the classification process. Upon assessment
with other classical machine learning methods such as
DT and RF. Better classification accuracy, precision,
recall, and F-score are achieved by the proposed DNN
model. Furthermore, the proposed DNN model achieves
the highest MAE of 94.9% as compared to DT and RF,
which attain an MAE score of 89.6% and 76.4%
respectively. This result suggests that the proposed
model has a prediction error of 5.1% which is fewer
when compared to DT and RF which has prediction
error of as much as 10.4% and 23.6%, thereby,
signifying the dominance of deep learning algorithm
over conventional machine learning algorithms in
salary classification and prediction task.
Keywords :
Principal Component Analysis, Random Forest, Deep Neural Network, Machine Learning, Deep Learning, and Decision Tree.
