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All Journal International Journal of Electrical and Computer Engineering JOIV : International Journal on Informatics Visualization
Mansor, Mahayaudin M.
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Computer Science & IT Electrical & Electronics Engineering

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Hybrid filtering methods for feature selection in high-dimensional cancer data Md Noh, Siti Sarah; Ibrahim, Nurain; Mansor, Mahayaudin M.; Yusoff, Marina
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6862-6871

Abstract

Statisticians in both academia and industry have encountered problems with high-dimensional data. The rapid feature increase has caused the feature count to outstrip the instance count. There are several established methods when selecting features from massive amounts of breast cancer data. Even so, overfitting continues to be a problem. The challenge of choosing important features with minimum loss in a different sample size is another area with room for development. As a result, the feature selection technique is crucial for dealing with high-dimensional data classification issues. This paper proposed a new architecture for high-dimensional breast cancer data using filtering techniques and a logistic regression model. Essential features are filtered out using a combination of hybrid chi–square and hybrid information gain (hybrid IG) with logistic regression as classifier. The results showed that hybrid IG performed the best for high-dimensional breast and prostate cancer data. The top 50 and 22 features outperformed the other configurations, with the highest classification accuracies of 86.96% and 82.61%, respectively, after integrating the hybrid information gain and logistic function (hybrid IG+LR) with a sample size of 75. In the future, multiclass classification of multidimensional medical data to be evaluated using data from a different domain.
Visualization Tools for Backward Elimination Technique in Multiple Regression Time Series Modelling of CO2 Emissions in Malaysia Mansor, Mahayaudin M.; Ibrahim, Nurain; Zakaria, Roslinazairimah; Suhaila, Jamaludin; Miswan, Nor Hamizah; Shaadan, Norshahida
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3012

Abstract

Understanding multiple regression time series modelling is crucial because the procedures involve intricate statistical methods. This study incorporates a flowchart that clearly illustrates the steps for modelling a response variable affected by several explanatory variables via the backward elimination technique. The first objective of this study is to utilise ten graphical tools, comprising charts and tables, for visual assessment to support formal evaluations in model diagnostics using R programming. The aim is to provide comprehensive insights and improve the overall understanding of the modelling procedures. The visualisation tools include criteria for multicollinearity, goodness-of-fit, and underlying assumptions of normality, homoscedasticity, zero serial correlation, and volatility in the residuals. The second objective involves implementing modelling procedures to obtain a well-specified model in a real-world context, demonstrating its practical value and implications. In this instance, the selected response variable is carbon dioxide (CO2) emissions, significantly contributing to global warming. In Malaysia, CO2 emissions increased continuously from 1990 to 2022, with an alarming average annual growth rate of 4.9%. The visual diagnostics have helped guide the elimination of some explanatory variables in the initial model and refined the models, resulting in a well-specified final model that is parsimonious and explains 98.6% of the variability in CO2 emissions. The final model suggests that high fossil fuel use and GDP per capita are contributing factors to increased CO2 emissions in Malaysia. The study recommends government action and investment in renewable energy to reduce CO2 emissions by 45% by 2030 and achieve net-zero emissions by 2050.
Co-Authors Ibrahim, Nurain Md Noh, Siti Sarah Miswan, Nor Hamizah Shaadan, Norshahida Suhaila, Jamaludin Yusoff, Marina Zakaria, Roslinazairimah