Toward Accelerated Training of Parallel Support Vector Machines Based on Voronoi Diagrams

Abstract

Typical applications of wireless sensor networks (WSN), such as in Industry 4.0 and smart cities, involves acquiring and processing large amounts of data in federated systems. Important challenges arise for machine learning algorithms in this scenario, such as reducing energy consumption and minimizing data exchange between devices in different zones. This paper introduces a novel method for accelerated training of parallel Support Vector Machines (pSVMs), based on ensembles, tailored to these kinds of problems. To achieve this, the training set is split into several Voronoi regions. These regions are small enough to permit faster parallel training of SVMs, reducing computational payload. Results from experiments comparing the proposed method with a single SVM and a standard ensemble of SVMs demonstrate that this approach can provide comparable performance while limiting the number of regions required to solve classification tasks. These advantages facilitate the development of energy-efficient policies in WSN.

Multi-Angle Fast Neural Tangent Kernel Classifier

“…The kernel idea is based on using kernels with feature spaces composed of logical propositions. Alfaro et al [37] introduced a novel method for accelerated training of parallel support vector machines based on ensembles. Song et al [38] proposed an accelerator for the SVM algorithm based on local geometrical information.…”

Quantum-Inspired Classification Based on Voronoi Tessellation and Pretty-Good Measurements

“…Then the contracted centroid of any class is constructed out to form the considered k Bloch vectors (line 5). The corresponding n density operators can be used to define the pretty-good measurement, according to (7), obtaining a quantum state discrimination procedure for any cluster of the tessellation (line 6). Given an unlabeled point x, there is calculation of the h nearest generator points in the input space (line 8) which correspond to as many pretty-good measurements.…”

“…The global decision is obtained by voting. Ensemble learning has been successfully used in diverse applications, such as text classification, speech recognition, sentiment analysis, protein-folding recognition, and streamflow forecasting [7].…”

Sensor Data Analytics: Challenges and Methods for Data-Intensive Applications

“…The last two works attach importance to the application of information gain and neural networks to detect activity profiles accurately. Alfaro et al [ 17 ] propose a new method to distribute the training process using the SVM algorithm, which can be applicable to Wireless Sensor Networks (WSN), aggregating the local contributions from individual sensors using Voronoi regions. Once again, this demonstrates the critical role of information aggregation in this kind of energy and location-aware sensor application.…”