Reda Younsi

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This paper describes an efficient randomised sphere cover classifier (αRSC), that reduces the training dataset size without loss of accuracy when compared to nearest neighbour classifiers. The motivation for developing this algorithm is the desire to have a non-deterministic, fast, instance-based classifier that performs well in isolation but is also ideal(More)
MOTIVATION Single nucleotide polymorphism (SNP) discovery is an important preliminary for understanding genetic variation. With current sequencing methods, we can sample genomes comprehensively. SNPs are found by aligning sequence reads against longer assembled references. De Bruijn graphs are efficient data structures that can deal with the vast amount of(More)
Motivation: Single nucleotide polymorphism (SNP) discovery is an important preliminary for understanding genetic variation. With current sequencing methods, we can sample genomes comprehensively. SNPs are found by aligning sequence reads against longer assembled references. De Bruijn graphs are efficient data structures that can deal with the vast amount of(More)
We propose and evaluate alternative ensemble schemes for a new instance based learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses instances into spheres, then bases classification on distance to spheres rather than distance to instances. The randomised nature of RSC makes it ideal for use in ensembles. We propose two ensemble(More)
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