Adaptive Fuzzy Learning Vector Quantization (AFLVQ) for Time Series Classification

Abstract

Over the past decade, a variety of research fields studied high-dimensional temporal data for pattern recognition, signal processing, fault detection and other purposes. Time series data mining has been constantly explored in the literature, and recent researches show that there are important issues yet to be addressed in the field. Currently, neural network based algorithms have been frequently adopted for solving classification problems. However, these techniques generally do not take advantage from expert knowledge about the processed data. In contrast, Fuzzy-based techniques use expert knowledge for performing data mining classification but they lack on adaptive behaviour. In this context, Hybrid Intelligent Systems (HIS) have been designed based on the concept of combining the adaptive characteristic of neural networks with the informative knowledge from fuzzy logic. Based on HIS, we introduce a novel approach for Learning Vector Quantization (LVQ) called Adaptive Fuzzy LVQ (AFLVQ) which consists in combining a Fuzzy-LVQ neural network with adaptive characteristics. In this paper, we conducted experiments with a time series classification problem known as Human Activity Recognition (HAR), using signals from a tri-axial accelerometer and gyroscope. We performed multiple experiments with different LVQ-based algorithms in order to evaluate the introduced method. We performed simulations for comparing three approaches of LVQ neural network: Kohonen’s LVQ, Adaptive LVQ and the proposed AFLVQ. From the results, we conclude that the proposed hybrid Adaptive-Fuzzy-LVQ algorithm outperforms several other methods in terms of classification accuracy and smoothness in learning convergence.