Ocena kreditne sposobnosti kombinovanjem različitih klasifikatora redukovanog skupa karakteristika
Sažetak
Finansijske i bankarske institucije u velikoj meri koriste metode ocene kreditne sposobnosti prilikom evaluacije zahteva za izdavanje kredita. Kreditna ocena daje informaciju o tome da li podnosilac zahteva pripada grupi sa dobrim rizikom ili grupi sa lošim rizikom. Ove odluke se zasnivaju na demografskim podacima o klijentima, ukupnom poslovanju klijenta sa bankom i istoriji otplate kredita koji je odobren podnosiocima. Prednosti korišćenja modela za određivanje kreditne ocene uključuju smanjenje troškova kreditne analize, omogućavanje bržih odluka o kreditu i smanjenje mogućeg rizika. Mnoge tehnike statističkog i mašinskog učenja, kao što su logistička regresija, mašine potpornih vektora, neuronske mreže i algoritmi stabla odlučivanja, korišćene su i nezavisno i kao hibridni modeli kreditnog ocenjivanja. Ovaj rad predlaže tehniku koja se bazira na kombinovanju sedam pojedinačnih modela kako bi se povećala preciznost klasifikacije. Izbor karakteristika se takođe koristi za odabir važnih atributa za klasifikaciju. Unakrsna klasifikacija je sprovedena pomoću tri particije podataka. U studiji je korišćen skup podataka o odobrenim kreditima u Nemačkoj koji ima 1000 instanci i 21 atribut. Rezultati eksperimenata su otkrili da je model kombinovanja omogućio veliku preciznost u odnosu na pojedine modele. U tri različite particije, model kombinovanja je u stanju da pravilno klasifikuje više od 80% korisnika kredita kao dobre poverioce. Pored toga, za partciju 70:30 postojao je dobar uticaj odabira karakteristika na tačnost klasifikatora. Rezultati su poboljšani za skoro sve pojedine modele uključujući model kombinovanja.
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