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1 |
A rejection inference technique based on contrastive pessimistic likelihood estimation for P2P lending
یک روش رد استنباط برمبنای تخمین احتمال بدبینی مخالف برای وام دهی P2P-2018 The majority of current credit-scoring models are built solely on accepted samples and thus cause sample bias. Sample bias is particularly severe in the peer-to-peer (P2P) lending domain due to its comparatively high rejection rate. Reject inference solves sample bias by inferring the possible outcomes of rejected samples and incorporating them into credit score modeling. This study addresses the problem of reject inference in a specific P2P lending domain from the perspective of semi-supervised learning. A novel reject inference method (CPLE-LightGBM) is proposed by combining the contrastive pessimistic likelihood estimation framework and an advanced gradient boosting decision tree classifier (LightGBM). The performance of the proposed CPLE-LightGBM method is validated on multiple datasets, and results demonstrate the efficiency of our proposal. Analysis of the influence of rejection rate on predictive accuracy reveals the usefulness of sampling in rejected datasets.
keywords: Big data applications |Contrastive pessimistic likelihood |Credit scoring |Data analytics |Gradient boosting decision tree estimation |Machine learning |P2P lending |Reject inference |Semi-supervised learning |
مقاله انگلیسی |
2 |
Semi-supervised learning for big social data analysis
یادگیری شبه نظارت شده برای تجزیه و تحلیل داده های اجتماعی بزرگ-2018 In an era of social media and connectivity, web users are becoming increasingly enthusiastic about inter
acting, sharing, and working together through online collaborative media. More recently, this collective
intelligence has spread to many different areas, with a growing impact on everyday life, such as in ed
ucation, health, commerce and tourism, leading to an exponential growth in the size of the social Web.
However, the distillation of knowledge from such unstructured Big data is, an extremely challenging task.
Consequently, the semantic and multimodal contents of the Web in this present day are, whilst being
well suited for human use, still barely accessible to machines. In this work, we explore the potential of a
novel semi-supervised learning model based on the combined use of random projection scaling as part of
a vector space model, and support vector machines to perform reasoning on a knowledge base. The latter
is developed by merging a graph representation of commonsense with a linguistic resource for the lexical
representation of affect. Comparative simulation results show a significant improvement in tasks such as
emotion recognition and polarity detection, and pave the way for development of future semi-supervised
learning approaches to big social data analytics.
Keywords: Semi-supervised learning ، Big social data analysis ، Sentiment analysis |
مقاله انگلیسی |