<Record><identifier xmlns="http://purl.org/dc/elements/1.1/">URN:NBN:SI:doc-F71I9RTM</identifier><date>2021</date><creator>Alsaif, Suleiman Ali</creator><creator>Hidri, Adel</creator><relation>documents/doc/F/URN_NBN_SI_doc-F71I9RTM_001.pdf</relation><relation>documents/doc/F/URN_NBN_SI_doc-F71I9RTM_001.txt</relation><format format_type="issue">2</format><format format_type="volume">45</format><format format_type="type">article</format><format format_type="extent">str. 223-230</format><identifier identifier_type="ISSN">0350-5596</identifier><identifier identifier_type="DOI">10.31449/inf.v45i2.3479</identifier><identifier identifier_type="COBISSID_HOST">79906051</identifier><identifier identifier_type="URN">URN:NBN:SI:doc-F71I9RTM</identifier><language>eng</language><publisher>Slovensko društvo Informatika</publisher><source>Informatica (Ljubljana)</source><rights>BY</rights><subject language_type_id="slv">neuravnoteženi podatki</subject><subject language_type_id="slv">ocena tveganja</subject><subject language_type_id="slv">računalništvo</subject><subject language_type_id="slv">umetna inteligenca</subject><title>Impact of data balancing during training for best predictions</title></Record>