An Artificial-Neural-Network-Based Multiple Classifier System for Knee-Joint Vibration Signal Classification

The knee-joint vibration or vibroarthrographic (VAG) signal could be used as an indicator with regard to the condition of degenerative articular cartilage surfaces of the knee joint. Analysis of VAG signals can assist in the screening for knee-joint pathology and help prevent unnecessary exploratory surgery. This paper proposes a multiple classifier system (MCS) based on artificial neural networks for the classification of VAG signals with statistical features. The multiple classifier system combines a group of component least-squares support vector machine classifiers with a linear and normalized fusion model. The fusion model minimizes the mean-squared error (MSE) of the MCS by solving the corresponding constrained quadratic programming problem, and the optimal weights are derived from the energy convergence process of a recurrent neural network. The results obtained with a data set of 89 VAG signals show that the proposed MCS can effectively reduce the classification error in terms of MSE. In addition, the proposed MCS also provides an area of 0.8230 under the receiver operating characteristics curve, which is much better in comparison with any one of the component networks with different input features, and also superior to the popular simple average or weighted average fusion method.