Using semi-supervised classifiers for credit scoring
نویسندگان
چکیده
منابع مشابه
Using semi-supervised classifiers for credit scoring
In credit scoring, low-default portfolios are those for which very little default history exists. This makes it problematic for financial institutions to estimate a reliable probability of a customer defaulting on a loan. Banking regulation (Basel II Capital Accord), and best practice, however, necessitate an accurate and valid estimate of the probability of default. In this article the suitabi...
متن کاملUsing DEA for Classification in Credit Scoring
Credit scoring is a kind of binary classification problem that contains important information for manager to make a decision in particularly in banking authorities. Obtained scores provide a practical credit decision for a loan officer to classify clients to reject or accept for payment loan. For this sake, in this paper a data envelopment analysis- discriminant analysis (DEA-DA) approach is us...
متن کاملSemi-supervised Instance Matching Using Boosted Classifiers
Instance matching concerns identifying pairs of instances that refer to the same underlying entity. Current state-of-the-art instance matchers use machine learning methods. Supervised learning systems achieve good performance by training on significant amounts of manually labeled samples. To alleviate the labeling effort, this paper presents a minimally supervised instance matching approach tha...
متن کاملA new corporate credit scoring system using semi-supervised discriminant analysis
Corporate credit scoring is important for investors and banks in risk management. However, the high dimensional data available from public financial statements make credit analysis difficult. To address the problem, dimensionality reduction is a key step to enhance scoring accuracy. By using semi-supervised discriminant analysis (SSDA) and support vector machines (SVMs), this study develops a n...
متن کاملSemi-Supervised Gaussian Process Classifiers
In this paper, we propose a graph-based construction of semi-supervised Gaussian process classifiers. Our method is based on recently proposed techniques for incorporating the geometric properties of unlabeled data within globally defined kernel functions. The full machinery for standard supervised Gaussian process inference is brought to bear on the problem of learning from labeled and unlabel...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Operational Research Society
سال: 2013
ISSN: 0160-5682,1476-9360
DOI: 10.1057/jors.2011.30