Navigating Complex Multiclass Classification in High-Dimensional Spaces: A Hybrid Approach

Large-scale tabular data classification is a critical task and the complexity arises from the vast amount of structured data generated in these fields, coupled with the challenges of high dimensionality and limited sample sizes. To address these challenges, advanced machine learning algorithms are required to analyze and categorize instances within these datasets effectively. In this work, we propose the usage of a state-of-the-art classifier called XBNet, which combines the strengths of tree-based classifiers and neural networks to tackle large tabular datasets. Our methodology is validated on a dataset with 64 dimensions and 11 classes, showcasing the model's capability to detect patterns and extract relevant features automatically. Furthermore, we employ K-Fold cross-validation to assess the model's performance, achieving an impressive training accuracy of 61.9% and a validation accuracy of 31.7%. These results surpass those of competing algorithms, confirming the superiority of our proposed methodology in handling large-scale tabular data classification tasks.