Abstract
Several phenomena are represented by directional—angular or periodic—data; from time references on the calendar to geographical coordinates. These values are usually represented as real values restricted to a given range (e.g., [ 0 , 2 π ) ), hiding the real nature of this information. In order to handle these variables properly in supervised classification tasks, alternatives to the naive Bayes classifier and logistic regression were proposed in the past. In this work, we propose directional-aware support vector machines. We address several realizations of the proposed models, studying their kernelized counterparts and their expressiveness. Finally, we validate the performance of the proposed Support Vector Machines (SVMs) against the directional naive Bayes and directional logistic regression with real data, obtaining competitive results.
Highlights
Several phenomena and concepts in real-life applications are represented by angular data or, as they are referred to in the literature, directional data
The results achieved by the von-Mises naive Bayes and directional Logistic Regression align with the results reported in the literature [4]
RMLP had the worst overall performance, and this effect was mostly apparent in small datasets where the number of directional features was in the same order of magnitude as the number of non-directional ones, like Colposcopy and eBay
Summary
Several phenomena and concepts in real-life applications are represented by angular data or, as they are referred to in the literature, directional data. Examples of data that may be regarded as directional include temporal periods (e.g., time of day, week, month, year, etc.), compass directions, dihedral angles in molecules, orientations, rotations, and so on. The application fields include the study of wind direction as analyzed by meteorologists and magnetic fields in rocks studied by geologists. The fact that zero degrees and 360 degrees are identical angles, so that for example 180 degrees is not a sensible mean of two degrees and 358 degrees, provides one illustration that special methods are required for the analysis of directional data. Directional data have been traditionally modeled with a wrapped probability density function, like a wrapped normal distribution, wrapped Cauchy distribution, or von Mises circular distribution. Measures of location and spread, like mean and variance, have been conveniently adapted to circular data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
