Semi-Supervised Learning Vector Quantization method enhanced with regularization for anomaly detection in air conditioning time-series data

摘要

Researchers of semi-supervised learning methods have been developing the family of Learning Vector Quantization models which originated from the well-known Self-Organizing Map algorithm. The models of this type can be characterized as prototype-based, self-explanatory and flexible.The thesis contributes to the development of one of the LVQ models – Semi-Supervised Relational Prototype Classifier for dissimilarity data. The model implementation is developed based on the related research work and thesis author findings, and applied to the task of anomaly detection from a real-time air condition data. We propose a regularization algorithm for gradient descent in order to achieve better convergence and a new strategy for initializing prototypes. We develop an innovative framework involving a human expert as a source of labeled data. The framework detects anomalies of environment parameters in both real-time and long-run observations and updates the model according to findings.The data set used for experiments is collected in real-time from sensors installed inside the Aalto Mechanical Engineering building located at Otakaari, 4, Espoo. Installation was done as a part of the project of VTT and Korean National Research Institute. The data consists of 3 main parameters – air temperature, humidity and CO2 concentration. Total number of deployed sensors is around 150. One month recorded data observations contains approximately 1.5M of data points.The results of the project demonstrate the efficiency of the developed regularized LVQ method for classification in given settings. Its regularized version generally overperforms its parent and various baseline methods on air conditioning, synthetic and UCI data. Together with the proposed classification framework, the system has shown its robustness and efficiency and is ready for deployment to a production environment.