Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science
Business Science Reference (Verlag)
978-1-7998-6659-6 (ISBN)
In today's digital world, the huge amount of data being generated is unstructured, messy, and chaotic in nature. Dealing with such data, and attempting to unfold the meaningful information, can be a challenging task. Feature engineering is a process to transform such data into a suitable form that better assists with interpretation and visualization. Through this method, the transformed data is more transparent to the machine learning models, which in turn causes better prediction and analysis of results. Data science is crucial for the data scientist to assess the trade-offs of their decisions regarding the effectiveness of the machine learning model implemented. Investigating the demand in this area today and in the future is a necessity.
The Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science provides an in-depth analysis on both the theoretical and the latest empirical research findings on how features can be extracted and transformed from raw data. The chapters will introduce feature engineering and the recent concepts, methods, and applications with the use of various data types, as well as examine the latest machine learning applications on the data. While highlighting topics such as detection, tracking, selection techniques, and prediction models using data science, this book is ideally intended for research scholars, big data scientists, project developers, data analysts, and computer scientists along with practitioners, researchers, academicians, and students interested in feature engineering and its impact on data.
Mrutyunjaya Panda is currently working as Reader in the P.G. Department of Computer Science and Applications, Utkal University, Vani Vihar, Bhubaneswar, Odisha, India. He has published about 75 papers in international and national journals and conferences. He has also published 10 book chapters, edited books in Springer and authored text books on: soft computing techniques and modern approaches of data mining; to his credit. His active areas of research include data mining, intrusion detection, social networking, wireless sensor networks, image processing, information retrieval and sentiment analysis. Harekrishna Misra is a professor in the area of IT & Information Systems Group at the Institute of Rural Management Anand, India. He is an Electronics and Communication Engineer, has Post-graduate Diploma in Business Management degree in Systems and Operations Management from XIM-Bhubaneswar, India and has Masters Degree in Software Systems from BITS-Pilani, India. He also holds a doctorate degree from Utkal University, Bhubaneswar, India in the area of Information Systems Management. He has around 28 years of experience in industry and academia in the fields of IT infrastructure management, communication, and networks. His current research interests include software engineering (process modelling), e-governance, information systems management in development organisations, e-Business for rural enterprises and ICT enabled value chain in rural enterprises. His current interests include information systems modelling related to business and development processes with citizen participation. He is a life member of Institution of Engineers (India), member of IEEE, Association of Computing Machinery (ACM) and Association of Information Systems, USA. He is in the reviewers' panel of the Computing Reviews-ACM. He has widely published in national and international reputed and refereed Journals. He has also participated in technical as well as programme committees and presented papers in national and international refereed academic conferences.
| Erscheinungsdatum | 15.01.2021 |
|---|---|
| Sprache | englisch |
| Maße | 216 x 279 mm |
| Gewicht | 633 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| ISBN-10 | 1-7998-6659-9 / 1799866599 |
| ISBN-13 | 978-1-7998-6659-6 / 9781799866596 |
| Zustand | Neuware |
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