The massive growth in the scale of data has been observed in recent years being a key
factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety …

Data preprocessing remains an important step in machine learning studies. This is because
proper preprocessing of imbalanced data can enable researchers to reduce defects as …

Abstract Recently, the following Discrimination-Aware Classification Problem was
introduced: Suppose we are given training data that exhibit unlawful discrimination; eg …

Data preprocessing is a major and essential stage whose main goal is to obtain final data
sets that can be considered correct and useful for further data mining algorithms. This paper …

This article describes design and application of feed-forward, fully-connected, three-layer
perceptron neural network model for computing the water quality index (WQI) 1 for Kinta …

In real-life data, information is frequently lost in data mining, caused by the presence of
missing values in attributes. Several schemes have been studied to overcome the …

The presence of missing values in real-world data is not only a prevalent problem but also
an inevitable one. Therefore, missing values should be handled carefully before the mining …

With the development of Internet of Things (IoT), more and more sensors, actuators and
mobile devices have been deployed into our daily lives. The result is that tremendous data …

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 …

Data preprocessing and cleansing play a vital role in data mining by ensuring good quality
of data. Data-cleansing tasks include imputation of missing values, identification of outliers …