Hybrid-Based Machine Incremental Learning in K-Nearest Neighbor Heterogeneous Drifting Environment

The ability to continuously learn over time by incorporating new information while holding onto previously acquired expertise is known as incremental learning (IL). Although this concept is fundamental to human learning, existing machine learning techniques have a significant propensity to forget prior experience by overwriting previously learned patterns from classes. The continuous learning of new information in K-nearest neighbor (KNN) with lazy learning strategies compounds to loss of old knowledge upon learning new information and stability-plasticity dilemma. The change in new data points and data distributions in unforeseen ways impacts KNN’s ability to adapt to changes in class label distribution, leading to concept drift. This experiment models a hybrid 3WDKNN-based incremental learning algorithm (ILA) designed for application in a heterogeneous and dynamically changing environment. This model addresses the limitations of KNN by overcoming computational costs and inefficiencies associated with loss of information in classes, while facilitating incremental learning to attain high predictive accuracy in crop yield datasets. The algorithm employs weighted voting to identify optimal assigned classes for the nearest neighbor and uses memory reconstruction strategy for class incremental learning until the memory is full without forgetting. Using weighted voting for the best assigned classes for the nearest neighbor, the algorithm uses a local mean vector to determine the best distances for the shortest-term incremental learning to achieve the highest performance accuracy in a concept drift environment. The hybrid 3WDKNN_ILA was developed and evaluated alongside advanced algorithms within the same dataset context. The model improves performance in incremental learning contexts by utilizing current concepts and minimizing errors on both current and recent data to avoid parameterization. The model achieves optimal efficient incremental learning by mitigating intentional loss and minimizing errors associated with valuable class information derived from aggregated mean values through class rectification and transfer. The hybrid model achieves the best efficient performance accuracy in all the tested weighted averages of 200W, 500W, and 1000W with tested set K values of 5, 9, and 13K. This hybrid model demonstrates performance accuracy of 97% at a value of 13K, whereas 3WD_KNN achieves 96% at 9K, HoKNN attains 89% at 13K, and 1IKNN reaches 88% at 9K accuracy, respectively. The integrated novelty in the hybrid 3WDKNN_ILA proves superior in terms of computational efficiency, accuracy, and high-level incremental performance and learning in comparison with other tested models of algorithms.