XuILVQ: A River Implementation of the Incremental Learning Vector Quantization for IoT
Martín González Soto, Bruno Fernández Castro, Rebeca P. Dı́az Redondo, Manuel Fernández‐Veiga · 2022
In machine learning, incremental learning algorithms provide a solution for models that need to dynamically adapt and react to their context by analyzing samples from data streams. These algorithms are especially suitable for Internet of Things (IoT) solutions where devices (like sensors/actuators) have low memory and computation capability. Within this context, we propose an implementation of an Incremental Learning Vector Quantization (ILVQ) algorithm compliant with the well-known River library standards. Our implementation was assessed according to its predicting capacity and also according to its memory and execution time consumption, showing outstanding results.