In practice, as a well-known statistical method, the logistic regression model is used to
evaluate the credit-worthiness of borrowers due to its simplicity and transparency in …

The need for controlling and effectively managing credit risk has led financial institutions to
excel in improving techniques designed for this purpose, resulting in the development of …

The use of fly ash in cementitious composites has gained popularity. However, assessing
the depth of wear (DW) of concrete requires expensive and destructive laboratory tests …

Credit risk and corporate bankruptcy prediction has widely been studied as a binary
classification problem using both advanced statistical and machine learning models …

Accurate prediction of high performance concrete (HPC) compressive strength is very
important issue. In the last decade, a variety of modeling approaches have been developed …

This paper investigates the use of wavelet ensemble models for high performance concrete
(HPC) compressive strength forecasting. More specifically, we incorporate bagging and …

Bagging is one of the well-known ensemble learning methods, which combines several
classifiers trained on different subsamples of the dataset. However, a drawback of bagging …

Classical time series analysis methods are not readily applicable to the series with missing
observations. To deal with the missingness in time series, the common approach is to use …

In classification, feature selection engineering helps in choosing the most relevant data
attributes to learn from. It determines the set of features to be rejected, supposing their low …

This paper presents a comprehensive review of the works done, during the 2000–2012, in
the application of data mining techniques in Credit scoring. Yet there isn't any literature in …