128 research outputs found
A survey of genetic algorithms for multi-label classification
In recent years, multi-label classification (MLC) has become an emerging research topic in big data analytics and machine learning. In this problem, each object of a dataset may belong to multiple class labels and the goal is to learn a classification model that can infer the correct labels of new, previously unseen, objects. This paper presents a survey of genetic algorithms (GAs) designed for MLC tasks. The study is organized in three parts. First, we propose a new taxonomy focused on GAs for MLC. In the second part, we provide an up-to-date overview of the work in this area, categorizing the approaches identified in the literature with respect to the taxonomy. In the third and last part, we discuss some new ideas for combining GAs with MLC
Nitrogen fertilization (15NH4NO3) of palisadegrass and residual effect on subsequent no-tillage corn
Caracterização técnica e desempenho hidráulico de quatro gotejadores autocompensantes utilizados no Brasil
Geostatistics applied to the study of the spatial distribution of Tibraca limbativentris in flooded rice fields
Qualitative characteristics of meat from confined crossbred heifers fed with lipid sources
Cirrose biliar em felinos associada à ectasia do ducto cístico e desvios portossistêmicos extra-hepáticos
Relações entre a câmara de Neubauer a espectrofotometria utilizadas para a determinação da concentração espermática de catetos (Pecari tajacu)
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