Novel hybrid classification model for multi-class imbalanced lithology dataset

Abstract

Deep learning methods and applications assist geologists in predicting and identifying lithologies in different surveys, hence lowering operational costs and uptime. This allows accurate data analysis and completion of scientific research on data obtained at geologically different places. This study used 4 lithologies datasets with high dimensionality and multiclass imbalance problems for analysis and classification. The imbalance of data classification is one of the most important problems facing current data analysis. Data imbalance can considerably influence classification performance, especially when dealing with other difficulty variables such as the presence of overlapping class distributions. This impact is especially obvious in multi-class conditions when mutual imbalance relations across classes complicate matters even further. Moreover, the problem of high dimensionality can lead to increased computing complexity and overfitting, and thus these issues can affect classification performance. To overcome these problems, we developed a new hybrid deep learning multi-class imbalanced learning method that combines Synthetic Minority Oversampling (SMOTE) to resample the data, and Recursive Feature Elimination (RFE) to identify the most useful predictive features. Finally, we believe that our developments can help improve geology research by providing accurate classification and rapid answers about interpreting data obtained in various study areas.