An Improved TA-SVM Method Without Matrix Inversion and Its Fast Implementation for Nonstationary Datasets

Recently, a time-adaptive support vector machine (TA-SVM) is proposed for handling nonstationary datasets. While attractive performance has been reported and the new classifier is distinctive in simultaneously solving several SVM subclassifiers locally and globally by using an elegant SVM formulation in an alternative kernel space, the coupling of subclassifiers brings in the computation of matrix inversion, thus resulting to suffer from high computational burden in large nonstationary dataset applications. To overcome this shortcoming, an improved TA-SVM (ITA-SVM) is proposed using a common vector shared by all the SVM subclassifiers involved. ITA-SVM not only keeps an SVM formulation, but also avoids the computation of matrix inversion. Thus, we can realize its fast version, that is, improved time-adaptive core vector machine (ITA-CVM) for large nonstationary datasets by using the CVM technique. ITA-CVM has the merit of asymptotic linear time complexity for large nonstationary datasets as well as inherits the advantage of TA-SVM. The effectiveness of the proposed classifiers ITA-SVM and ITA-CVM is also experimentally confirmed.

SSC: a classifier combination method based on signal strength

We propose a new classifier combination method, the signal strength-based combining (SSC) approach, to combine the outputs of multiple classifiers to support the decision-making process in classification tasks. As ensemble learning methods have attracted growing attention from both academia and industry recently, it is critical to understand the fundamental issues of the combining rule. Motivated by the signal strength concept, our proposed SSC algorithm can effectively integrate the individual vote from different classifiers in an ensemble learning system. Comparative studies of our method with nine major existing combining rules, namely, geometric average rule, arithmetic average rule, median value rule, majority voting rule, Borda count, max and min rule, weighted average, and weighted majority voting rules, is presented. Furthermore, we also discuss the relationship of the proposed method with respect to margin-based classifiers, including the boosting method (AdaBoost.M1 and AdaBoost.M2) and support vector machines by margin analysis. Detailed analyses of margin distribution graphs are presented to discuss the characteristics of the proposed method. Simulation results for various real-world datasets illustrate the effectiveness of the proposed method.

Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine

Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM) where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off. The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed. The load classification method is composed of two processes including frequency feature analysis and support vector machine. The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods. The load classification method has achieved more than ninety percent recognition rate.