Enhancing Classification Algorithm Accuracy through Hybrid Pre-Processing Strategies
Ace C. Lagman
a
,
Jeneffer A. Sabonsolin
a
,
Arvin N. Natividad
b
,
Diosdado T. Lleno
c
,
Reden Paul L. Rivera
d
,
Ronnel C. Delos Santos
d
a CCSMA, FEU Institute of Technology, Manila, Philippines
b Vice-President for Administrative and Financial Affairs, Southern Luzon State University, Quezon, Philippines
c Vice President for Administration and Finance, Bestlink College of the Philippines, Caloocan
d Pampanga State University, Pampanga
2025 23rd International Conference on ICT and Knowledge Engineering (ICT&KE), (2025), pp. 1-7
Abstract: The accuracy of classification algorithms is significantly influenced by the quality and structure of input data. 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 preprocessing 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 the classification results. Through the promotion of intelligent, data-driven solutions that can be applied in multiple sectors, the findings support the Sustainable Development Goal 9 (SDG 9), which focuses on Industry, Innovation and Infrastructure.