Ensemble Learning is a powerful machine learning model that combines the multiple learning models to generate the learning results with improved classification or prediction performance than the individual learning model. The significance of applying ensemble over a model is higher performance in making predictions and robustness in reducing the dispersion of predictions. The main goal of ensemble learning is to improve the predictive performance of a single model by processing the data in multiple learning models or combining the results of multiple learning models. Ensemble learning is mainly used to select the optimal feature in the sample, reduce the over-fitting problem in models, incremental learning, and correct the bias and variance.
The main methods of ensemble learning are Boosting, Bagging, and stacking. Boosting: Boosting is a type of ensemble learning technique used to decrease the bias and variance of the model. Bagging: Bagging is another type of ensemble learning technique it is applied to the model for increasing accuracy and stability. It is also used to reduce the over-fitting problem and variance in the model. Stacking: It involves the process of improving the prediction of the model. Random Forest, Gradient Boosting, Decision Tree are the popular ensemble learning algorithms. Application areas of ensemble learning are Remote sensing, Computer security, Face and Emotion recognition, Fraud detection, Financial Decision making, and Medicine. Recent research areas of ensemble learning are pattern classification, software defect prediction, malware detection, disease prediction, and intrusion detection.
• In ensemble learning, the complementary information of base models is effectively utilized to improve the better performance of the overall model and also improves the generalization of the learning system.
• In the context of feature extraction, features are extracted through a diversity of projections on data and fuse results with various voting mechanisms.
• The dynamic ensemble learning algorithm is widely used for real-world classification problems.
• Determining the appropriate model size and reducing the complexity of the model becomes a significant challenge for increasing the training speed in ensemble learning.
• It is necessary to handle multiple-type data, such as semi-structured and unstructured, or continuous and discrete, for expanding the practical applications of ensemble classification.