Transformation of nominal features into numeric in supervised multi-class problems based on the weight of evidence parameter

Abstract

Abstract-Machine learning has received increased interest by both the scientific community and the industry. Most of the machine learning algorithms rely on certain distance metrics that can only be applied to numeric data. This becomes a problem in complex datasets that contain heterogeneous data consisted of numeric and nominal (i.e. categorical) features. Thus the need of transformation from nominal to numeric data. Weight of evidence (WoE) is one of the parameters that can be used for transformation of the nominal features to numeric. In this paper we describe a method that uses WoE to transform the features. Although the applicability of this method is researched to some extent, in this paper we extend its applicability for multi-class problems, which is a novelty. We compared it with the method that generates dummy features. We test both methods on binary and multi-class classification problems with different machine learning algorithms. Our experiments show that the WoE based transformation generates smaller number of features compared to the technique based on generation of dummy features while also improving the classification accuracy, reducing memory complexity and shortening the execution time. Be that as it may, we also point out some of its weaknesses and make some recommendations when to use the method based on dummy features generation instead.

Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks

“…First, there are sensory data in time-series format, thus requiring feature extraction and alignment, which could be done with time-series analysis approaches, such as that of Zdravevski et al [ 39 ], or by using deep learning approaches, such as that of Ordónez and Roggen [ 40 ]. For medical records, questionnaires, and other nominal data, approaches like that of Zdravevski et al [ 41 ], which convert these data into numeric data, are required. These diverse data streams need to be fused, which poses another set of challenges, as eloquently described in [ 42 ].…”

Machine Learning Techniques with ECG and EEG Data: An Exploratory Study

“…The machine learning method implemented was a neural network, i.e., multiplayer perceptron, implemented with the WEKA software [25] with the following details: weights to the input and output neurons. It also supports different attribute transformation methods, including ones for handling nominal and numeric data, which is important for medical datasets, which frequently encounter mixed data types [32,33].…”

Activity-Aware Fall Detection and Recognition Based on Wearable Sensors

“…The recent advancements of the technology (Cardinale and Varley, 2017;Kim et al, 2016;Ni Scanaill et al, 2011;Patel et al, 2012;Sousa et al, 2015) and the presence of sensors in the commonly used off-theshelf mobile devices (Shahriyar et al, 2010;Stankevich et al, 2012;Steele, 2011;Tian et al, 2019;Ventola, 2014) allow the development of solutions for the identi cation of the human daily living activities in order to monitor its lifestyles, e.g., the creation of a Personal Digital Life Coach (Garcia, 2016). However, the accuracy of these systems and their resilience of fails is essential for the recognition of activities in different environments (Dimitrievski et al, 2016a;Pires et al, 2017Pires et al, , 2018aZdravevski et al, 2015). In general, sportspeople, older adults, and other persons with special needs are living in conditions with bad network connection, but the development of solutions for this type of people is vital to improve their quality of life (Dimitrievski et al, 2016b;Sendra et al, 2012;Seneviratne et al, 2017).…”