Integrating Hybrid Pre-Processing Strategies to Optimize Classification Algorithm Performance
2026 14th International Conference on Information and Education Technology (ICIET), (2026), pp. 194-199
Jeneffer A. Sabonsolin
a
,
Jameson Buhayang
b
,
Christian I. Cabrera
c
,
Edwardo Miguel Roldan
a
,
Philip I Doctor
d
,
Jaevier A. Villanueva
e
a FEU Institute of Technology, Manila, Philippines
b Biliran Province State University, Naval Biliran, Philippines
c Mindoro State University, Mindoro, Philippines
d Pampanga State University, Manila, Philippines
e Rizal Technological University, Manila, Philippines
Abstract: The quantity and organization of input data have significant effects on the accuracy of classification algorithms are. In this light, effective pre-processing is crucial for boosting the generalization capabilities of supervised machine learning models. This study addresses key challenges in data preparation, including the treatment of continuous attributes, imputation of missing values, and management of high-dimensional features. To overcome these obstacles, we propose an innovative hybrid pre-processing strategy that synthesizes multiple techniques into a unified framework. By tailoring specific methods to the characteristics of diverse datasets, this hybrid approach enhances both the accuracy and robustness of classification outcomes. By promoting innovative data-driven solutions that are relevant to a variety of industries, the findings help achieve Sustainable Development Goal 9: Industry, Innovation, and Infrastructure.