Informed energy decision making requires effective software, high-quality input data, and a suitably trained user community. Developing these resources can be expensive and time consuming. Even when data and tools are intended for public re-use they often come with technical, legal, economic and...

Software Defect Prediction (SDP) models are used to predict, whether software is clean or buggy using the historical data collected from various software repositories. The data collected from such repositories may contain some missing values. In order to estimate missing values, imputation...

In this paper, we consider the problem of missing data, and develop an ensemble-network model for handling the missing data. The proposed method is based on utilizing the inherent uncertainty of the missing records in generating diverse training sets for the ensemble's networks. Specifically we...

Customer management activities at firms involve making consistent decisions over time, about: (a) which customers to select for targeting, (b) determining the level of resources to be allocated to the selected customers, and (c) selecting customers to be nurtured to increase future...

Purpose Class imbalance learning, which exists in many domain problem datasets, is an important research topic in data mining and machine learning. One-class classification techniques, which aim to identify anomalies as the minority class from the normal data as the majority class, are one...

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 approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an...