3.1.2 Data preprocessing After data collection, the major challenge is to preprocess the data. In data preprocessing, the collected data consist of missing values, and therefore, for achieving better classifica
The classifier can tell which class the data belong to. The multiclass classification-based methods can also be further classified into two subcategories, i.e. support vector machine-based and artificial neural networks-based. Sign in to download hi-res image Fig. 8. A general scheme of the ...
Statistical mixture-of-experts models are often used for data analysis tasks such as clustering, regression and classification. We will consider two mixture-of-experts models, the shared mixture classifier and the hierarchical mixture-of-experts classifier. We discuss the initialisation and optimisation ...
This work examines the application of machine learning (ML) algorithms to evaluate dissolved gas analysis (DGA) data to quickly identify incipient faults in oil-immersed transformers (OITs). Transformers are pivotal equipment in the transmission and dist
of learning a data representation from raw data using DL methods. In addition, feeding the learned data representation to the ML classifier helps to decrease the demand of having large amounts of data for training the classifier. The hybrid approach can also help to increase the interpretability ...
The classification problem is closely related to the clustering problem discussed in Chaps. 6 and 7. While the clustering problem is that of determining similar groups of data points, the classification problem is that of learning the structure of a data set of examples, already partitioned into...
The ResNet-R &H can achieve a testing accuracy of 97.6%, which demonstrates a significant enhancement of 4.0% and 7.2% compared to the distinct utilization of hyperspectral data and RGB data, respectively. Overall, this research is significant in providing a unique, efficient, and more accurate...
Data classification can be a complex process, but it doesn't have to be. This guide will walk you through the steps of classifying your data in a simple and easy-to-understand way.
Sensitive data is important to identify, to ensure the right people have access to the right data. However, sensitivity is just one dimension of data classification. In order to get the full potential from data classification, and value from your data, you need to combine categorization with se...
in turn, provides the structure of how the business should interpret the metadata. While DCS can label document sensitivity, it goes beyond this functionality to address the complexities of today's regulatory environment. By leveraging DCS, organizations can achieve complete data protection and navigate...