Title: Enhancing Deep Learning Models for Imbalanced Datasets. 

Authors: Dr. Josephine

Abstract: Deep learning has achieved remarkable success in various domains, but its performance can be significantly hampered when dealing with imbalanced datasets, where one class has substantially more instances than others. This imbalance can bias the learning process, leading to models that are predominantly predictive of the majority class and poorly generalize to the minority class, which is often the class of greater interest.  This paper explores several techniques to enhance deep learning models for imbalanced datasets. We investigate the effectiveness of data-level approaches, such as oversampling the minority class using techniques like SMOTE and ADASYN, and undersampling the majority class using methods like random undersampling and Tomek Links.  We also examine cost-sensitive learning, where the loss function is modified to penalize misclassifications of the minority class more heavily. Furthermore, we explore ensemble methods, such as bagging and boosting, to combine multiple models trained on different subsets of the data or with varying class weights.  We evaluate the performance of these techniques on several benchmark imbalanced datasets, comparing metrics like precision, recall, F1-score, and AUC-ROC, which are more informative than accuracy in imbalanced scenarios.  Our results demonstrate the varying effectiveness of these methods depending on the dataset characteristics and highlight the importance of careful selection and tuning of techniques to achieve optimal performance with deep learning on imbalanced data.  Finally, we discuss open challenges and future research directions in this critical area.

Keywords: Time Series Forecasting, Natural Language Processing, Object Detection

International Journal of Applied Pattern Recognition, Vol. 6, No. 2, 2018 (Special Issue)

Received: 12 Feb 2018

Accepted: 24 April 2018

Published online: 01 June 2018