An active learning paradigm based on a priori data reduction and organization

• Published in: Expert systems with applications Vol. 41; no. 14; pp. 6086 - 6097
• Main Authors: Saito, Priscila T.M., de Rezende, Pedro J., Falcão, Alexandre X., Suzuki, Celso T.N., Gomes, Jancarlo F.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 15.10.2014; Elsevier
• Subjects: Active learning; Applied sciences; Artificial intelligence; Classification; Computation; Computer science; control theory; systems; Data mining; Data processing. List processing. Character string processing; Data reduction; Exact sciences and technology; Expert systems; Image annotation; Information systems. Data bases; Iterative methods; Learning; Machine learning; Memory organisation. Data processing; Organizations; Pattern recognition; Pattern recognition. Digital image processing. Computational geometry; Software; Strategy; Pattern recognition; Data mining; Active learning; Image annotation; Machine learning; Image processing; High precision; Very large databases; Active system; Efficiency; User interface; Classification; Learning algorithm; Small medium sized firm; Data analysis; Process selection; Data reduction; Interactive system; Dimension reduction; Experimental result; Supervised learning; Learning (artificial intelligence); Small sample; Artificial intelligence; Indexing

•A novel active learning paradigm, called DROP, based on a priori data reduction and organization.•DROP does not require classification and reorganization of all non-annotated samples in the dataset at each iteration.•The proposed paradigm allows to achieve high accuracy quickly with minimum user in...