A Personal Credit Rating Prediction Model Using Data Mining in Smart Ubiquitous Environments

Bae, Jae Kwon; Kim, Jin-Hwa

doi:10.1155/2015/179060

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…International Journal of Computing information [6]. A wide range of data is transforming into knowledge via data mining, which can be seen as a result of the natural development of information technology [7].…”

Section: Print Issn 1727-6209 On-line Issn 2312-5381mentioning

confidence: 99%

Prediction of Credit Card Payment Next Month Through Tree Net Data Mining Techniques

Hussein¹,

Gheni²,

Oleiwi³

et al. 2020

IJC

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A number of research initiatives have recently been launched around the world regarding the conceptualization, specification, design and development principles of the future use of credit cards, storing secret information on them, while most time we use them for online payment. In addition, if it has enough money, we can pay for what we need at any time. Therefore, the goal of this proposed research is to use data mining techniques to predict credit card payment next month. Our proposed system contains five steps: (a) find the suitable database from the internet because this database is not available in Iraq, (b) pre-process the credit card database based on person correlation matrix to determine which feature is less correlated with other to remove it and reduce the time of prediction, (c) split pre-processing database into two parts training and testing dataset, (d) apply TreeNet prediction data mining techniques (TPDMT) on training dataset to test if we need payment next month or do not, find the optimal tree. TreeNet based on Boosting Machine usually makes the predictor to use Decision Trees (DTs). (e ) Finally, pass the testing dataset on the optimal tree results from TPDMT, then using the five measures related to confusion matrix to evaluate the results including “Accuracy (AC), recall or true positive rate (TP), precision (P), F-measure (considers both precision and recall) and Fb”.

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Section: Print Issn 1727-6209 On-line Issn 2312-5381mentioning

confidence: 99%

Prediction of Credit Card Payment Next Month Through Tree Net Data Mining Techniques

Hussein¹,

Gheni²,

Oleiwi³

et al. 2020

IJC

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“…Traditionally, the logistic model was often used to predict defaults and today is still useful for benchmarking thanks to its simplicity, interpretability and dependability [34,4,14]. However, more sophisticated and innovative approaches are also used, like neural networks [34,38,15], smart ubiquitous data mining [4], theory of three-way decisions [20], and theory of survival [14]. In our paper, we introduce a novel approach that starts with creating a large number of features (over 6,000 here) and then reducing them to a few well performing subsets.…”

Section: Credit Risk Managementmentioning

confidence: 99%

“…Banks and companies traditionally tackle this problem by calculating credit scores or probability of default for each of the potential clients. For individual customers data sets used to predict a customer's probability of default include demographic data, loan and credit information [4], social media [10,38] or mobile phone data [6].…”

Section: Credit Risk Managementmentioning

confidence: 99%

Predicting complex user behavior from CDR based social networks

Doyle

Herga

Dipple

et al. 2019

Information Sciences

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Call Detail Record (CDR) datasets provide enough information about personal interactions of cell phone service customers to enable building detailed social networks. We take one such dataset and create a realistic social network to predict which customer will default on payments for the phone services, a complex behavior combining social, economic, and legal considerations. After extracting a large feature set from this network, we find that each feature poorly correlates with the default status. Hence, we develop a sophisticated model to enable reliable predictions. Our main contribution is a methodology for building complex behavior models from very large sets of diverse features and using different methods to choose those features that perform best for the final model. This approach enables us to identify the most efficient features for our problem which, unexpectedly, are based on the number of unique users with whom the given user communicates around the Christmas and New Year's Eve holidays. In general, features based on the number of close ties maintained by a user perform better than others. Our resulting models significantly outperform the methods * Corresponding author currently published in the literature. The paper contributes also a systematic analysis of properties of the network derived from CDR.

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“…However, this assumption is rarely possible in real-world and potentially can cause problems in terms of data privacy [16]. Suppose that the label of source data contains bio-metric information, e.g., face, fingerprint, iris pattern, or confidential information about specific individuals [17,18,19,20]. From the security viewpoint, this kind of sensitive label can serve as an identifier for each sample or individual; thus, improper disclosure of data with corresponding (a) (b) Figure 1: Normalized self-entropy statistics of target samples that passed through the pre-trained source model (ResNet-50) on Ar ⇒ Cl in Office-Home.…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptation without Source Data

Kim¹,

Cho²,

Han³

et al. 2020

Preprint

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Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase. However, such an assumption is rarely plausible in real cases and possibly causes data-privacy issues, especially when the label of the source domain can be a sensitive attribute as an identifier. To avoid accessing source data which may contain sensitive information, we introduce source data-free domain adaptation (SFDA). Our key idea is to leverage a pre-trained model from the source domain and progressively update the target model in a self-learning manner. We observe that target samples with lower self-entropy measured by the pre-trained source model are more likely to be classified correctly. From this, we select the reliable samples with the self-entropy criterion and define these as class prototypes. We then assign pseudo labels for every target sample based on the similarity score with class prototypes. Further, to reduce the uncertainty from the pseudo labeling process, we propose set-to-set distance-based filtering which does not require any tunable hyperparameters. Finally, we train the target model with the filtered pseudo labels with regularization from the pre-trained source model. Surprisingly, without direct usage of labeled source samples, our SFDA outperforms conventional domain adaptation methods on benchmark datasets. Our code is publicly available at https://github.com/youngryan1993/SFDA-Domain-Adaptation-without-Source-Data.

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A Personal Credit Rating Prediction Model Using Data Mining in Smart Ubiquitous Environments

Cited by 8 publications

References 8 publications

Prediction of Credit Card Payment Next Month Through Tree Net Data Mining Techniques

Prediction of Credit Card Payment Next Month Through Tree Net Data Mining Techniques

Predicting complex user behavior from CDR based social networks

Domain Adaptation without Source Data

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