Multi-label semi-supervised classification through optimum-path forest
نویسندگان
چکیده
منابع مشابه
Supervised Pattern Classification Using Optimum-Path Forest
We present a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), and describe one of its classifiers developed for the supervised learning case. This classifier does not require parameters and can handle some overlapping among multiple classes with arbitrary shapes. The method reduces the pattern recognition problem into the computation of an optimum-path forest in ...
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Although one can find several pattern recognition techniques out there, there is still room for improvements and new approaches. In this book chapter, we revisited the Optimum-Path Forest (OPF) classifier, which has been evaluated over the last years in a number of applications that consider supervised, semi-supervised and unsupervised learning problems. We also presented a brief compilation of...
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Data acquisition technologies can provide large datasets with millions of samples for statistical analysis. This creates a tremendous challenge for pattern recognition techniques, which need to be more efficient without loosing their effectiveness. We have tried to circumvent the problem by reducing it into the fast computation of an optimum-path forest (OPF) in a graph derived from the trainin...
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We present an approach for supervised classification, which interprets a training set as a complete graph, identifies prototypes in all classes, and computes an optimum-path forest rooted at them. The class of a sample in a tree is assumed to be the same of its root. A test sample is classified by identifying which tree would contain it. We show how to improve performance from the errors on an ...
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ژورنال
عنوان ژورنال: Information Sciences
سال: 2018
ISSN: 0020-0255
DOI: 10.1016/j.ins.2018.06.067