The Internet of Things (IoT) is enabled by the latest developments in smart sensors,
communication technologies, and Internet protocols with broad applications. Collecting data …

Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …

The presence of missing data is a challenging issue in processing real-world datasets. It is
necessary to improve the data quality by imputing the missing values so that effective …

With the development of industry 4.0, the credit data of SMEs are characterized by a large
volume, high speed, diversity and low-value density. How to select the key features that …

Recent work on deep learning for tabular data demonstrates the strong performance of deep
tabular models, often bridging the gap between gradient boosted decision trees and neural …

Credit risk assessment is critical for loan approval and risk management of banks. However,
the problem of missing credit risk data may greatly reduce the effectiveness of the …

We propose a bi-objective algorithm based on the k-nearest neighbors (biokNN) method to
perform imputation of missing values for data with multilevel structures with continuous …

Purpose The primary aim of this study is to review the studies from different dimensions
including type of methods, experimentation setup and evaluation metrics used in the novel …

The presence of missing data is a common and pivotal issue, which generally leads to a
serious decrease of data quality and thus indicates the necessity to effectively handle …

In real cases, missing values tend to contain meaningful information that should be acquired
or should be analyzed before the incomplete dataset is used for machine learning tasks. In …