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Wyszukujesz frazę "Credit Scoring" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Parameter Estimation Using LU Decomposition in the Logistic Regression Model for Credit Scoring Analysis
Autorzy:
Rahmani, Ulfa
Pribadi, Diantiny Mariam
Purwani, Sri
Powiązania:
https://bibliotekanauki.pl/articles/1031893.pdf
Data publikacji:
2020
Wydawca:
Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
Tematy:
Credit Scoring
LU Decomposition
Logistic Regression
Problem Loans
Opis:
Banking is a financial institution that has a very important role in economic and trade activities which is useful for channeling funds in the form of loans to the public who need fresh funds for business in the hope of helping to improve the people's economy. In the loan process, banks are often exposed to risks known as credit risk or non-performing loans. Therefore, a credit analysis is performed by estimating the parameters using LU Decomposition in the Logistic Regression model. In this paper, the data used are data about cooperative financial services in Indonesia. Variables taken in the study are including the age of debtors (X1), family dependents (X2), the amount of savings (X3), the value of collateral (X4), the amount of income per month (X5), given the credit limit (X6), take home pay (X7), and the loan term (X8).
Źródło:
World Scientific News; 2020, 140; 1-11
2392-2192
Pojawia się w:
World Scientific News
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Recursive-rule extraction algorithm with J48graft and applications to generating credit scores
Autorzy:
Tanaka, Y.
Takagi, T.
Saito, T.
Iiduka, H.
Kikuchi, H.
Bologna, G.
Mitra, S.
Powiązania:
https://bibliotekanauki.pl/articles/91868.pdf
Data publikacji:
2016
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
Rule Extraction
Credit Scoring
Re-RX algorithm
J48graft
Opis:
The purpose of this study was to generate more concise rule extraction from the Recursive- Rule Extraction (Re-RX) algorithm by replacing the C4.5 program currently employed in Re-RX with the J48graft algorithm. Experiments were subsequently conducted to determine rules for six different two-class mixed datasets having discrete and continuous attributes and to compare the resulting accuracy, comprehensibility and conciseness. When working with the CARD1, CARD2, CARD3, German, Bene1 and Bene2 datasets, Re-RX with J48graft provided more concise rules than the original Re-RX algorithm. The use of Re-RX with J48graft resulted in 43.2%, 37% and 21% reductions in rules in the case of the German, Bene1 and Bene2 datasets compared to Re-RX. Furthermore, the Re-RX with J48graft showed 8.87% better accuracy than the Re-RX algorithm for the German dataset. These results confirm that the application of Re-RX in conjunction with J48graft has the capacity to facilitate migration from existing data systems toward new concise analytic systems and Big Data.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 1; 35-44
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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