شماره ركورد كنفرانس :
3297
عنوان مقاله :
Developing a Fast Supervised Optimum-path Forest Based on Coreset
عنوان به زبان ديگر :
Developing a Fast Supervised Optimum-path Forest Based on Coreset
پديدآورندگان :
Bostani Hamid Young Researchers and Elite Club - South Tehran Branch - Islamic Azad University , Sheikhan Mansour Department of Communication Engineering - South Tehran Branch - Islamic Azad University - Tehran , Mahboobi Behrad Department of Electrical and Computer Engineering - Science and Research Branch - Islamic Azad University - Tehran
كليدواژه :
supervised machine learning , coreset , optimum-path forest , component
عنوان كنفرانس :
نوزدهمين سمپوزيوم بين المللي هوش مصنوعي و پردازش سيگنال
چكيده لاتين :
Optimum-path forest (OPF) is an effective graphbased
machine learning that simplifies the pattern recognition
problems into the partitioning the corresponding derived graphs
of the input datasets. The amounts of the samples in the input
datasets and, consequently the size of the node set of their
corresponding derived graphs has a major effect on the speed of
OPF. In this study a novel version of OPF is introduced which
utilizes coreset approach for reducing the scale of the input
dataset. From the aspect of the computational geometry, coreset
is a small set of points that includes the best representative points
of the original point set with regard to a geometric objective
function. Our method finds the most informative vertices
(samples) by proposing a novel incremental coreset construction
algorithm. The experimental results of the proposed method
reduces the input data samples, and the execution times of the
construction and the classification phases of OPF by 80%, 60%,
and 12%, respectively, in contrast to the traditional OPF.
