Error Detection using Orthogonal Code

S.Panse, Ms.Mrunal

doi:10.9790/3021-04350711

Cited by 1 publication

(1 citation statement)

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“…When zeros are added in, a ij ∈ {−1, 0, 1}, it becomes even more difficult. Work in signal processing may be of limited applicability because coding matrices are typically comprised of 0's and 1's rather than −1's and +1's (Hedayat et al, 1999;Panse et al, 2014). In our case, a further restriction is that columns must contain both positive and negative elements, or:…”

Section: Constructing the Coding Matrixmentioning

confidence: 99%

Solving for multi-class using orthogonal coding matrices

Mills¹

2019

SN Appl. Sci.

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A common method of generalizing binary to multi-class classification is the error correcting code (ECC). ECCs may be optimized in a number of ways, for instance by making them orthogonal. Here we test two types of orthogonal ECCs on seven different datasets using three types of binary classifier and compare them with three other multi-class methods: 1 vs. 1, one-versus-the-rest and random ECCs. The first type of orthogonal ECC, in which the codes contain no zeros, admits a fast and simple method of solving for the probabilities. Orthogonal ECCs are always more accurate than random ECCs as predicted by recent literature. Improvments in uncertainty coefficient (U.C.) range between 0.4-17.5% (0.004-0.139, absolute), while improvements in Brier score between 0.7-10.7%. Unfortunately, orthogonal ECCs are rarely more accurate than 1 vs. 1. Disparities are worst when the methods are paired with logistic regression, with orthogonal ECCs never beating 1 vs. 1. When the methods are paired with SVM, the losses are less significant, peaking at 1.5%, relative, 0.011 absolute in uncertainty coefficient and 6.5% in Brier scores. Orthogonal ECCs are always P. Mills (2019) SN Applied Sciences 1(11):1451 2 the fastest of the five multi-class methods when paired with linear classifiers. When paired with a piecewise linear classifier, whose classification speed does not depend on the number of training samples, classifications using orthogonal ECCs were always more accurate than the the remaining three methods and also faster than 1 vs. 1. Losses against 1 vs. 1 here were higher, peaking at 1.9% (0.017, absolute), in U.C. and 39% in Brier score. Gains in speed ranged between 1.1% and over 100%. Whether the speed increase is worth the penalty in accuracy will depend on the application.

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Section: Constructing the Coding Matrixmentioning

confidence: 99%

Solving for multi-class using orthogonal coding matrices

Mills¹

2019

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

Error Detection using Orthogonal Code

Cited by 1 publication

References 3 publications

Solving for multi-class using orthogonal coding matrices

Solving for multi-class using orthogonal coding matrices

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