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Multi-label learning is a hot spot in machine learning and data mining. The multi-label learning model can predict one or more labels for a test instance. PT5 is a effective method to solving multi-label learning problem, it is critical to set an optimal threshold in this method. More error labels will be predicted if a lower threshold was set, and the labels will be predicted not entire if a big...
Partial least square (PLS) is the most commonly used algorithm for near infrared (NIR) modeling. NIR modeling features that it's cheap, easy and fast to measure the NIR spectroscopy while expensive, difficult and time-consuming to measure the reference value for this spectroscopy. PLS often faces the challenge of that limited samples are available in training set to build a predicative model. To tackle...
Structure learning for Bayesian networks classifier is NP-hard problem, K2 algorithm is one of efficacious and accurate algorithm. K2 algorithm confirms the order of nodes firstly. To a certain extent this limits in non-information. This paper purposes a new heuristic Bayesian networks classifier structure learning G2 algorithm. G2 algorithm use NB and TAN structure which learns as heuristic information,...
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