Aggregated classifiers and variable selection to improve prediction capability
作者： S Borra
DOI:
关键词: Mathematics 、 Artificial intelligence 、 Pattern recognition 、 Humanities
摘要: Riassunto: Molti lavori comparativi, sia teorici sperimentali, hanno mostrato come i classificatori aggregati possono far aumentare significativamente la capacita di previsione rispetto a singoli modelli classificazione. Negli ultimi anni sono stati proposti diversi metodi costruzione aggregati, basati su tecniche ricampionamento dei dati, sull’estrazione sottoinsiemi variabili esplicative. In entrambi casi, e stato dimostrato che una procedura selezione delle interna agli algoritmi stessi puo incrementare notevolmente del modello. Una puo, inoltre, agevolare l’individuazione piu importanti. questo lavoro descriviamo recenti per aggregati.
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