Hierarchical Feature Selection For Knowledge Discovery

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Hierarchical Feature Selection for Knowledge Discovery

Author : Cen Wan
Publisher : Springer
ISBN 13 : 3319979191
Total Pages : 128 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Hierarchical Feature Selection for Knowledge Discovery by : Cen Wan

Download or read book Hierarchical Feature Selection for Knowledge Discovery written by Cen Wan and published by Springer. This book was released on 2018-11-29 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties of this book are three-fold. To begin with, this book discusses the hierarchical feature selection in depth, which is generally a novel research area in Data Mining/Machine Learning. Seven different state-of-the-art hierarchical feature selection algorithms are discussed and evaluated by working with four types of interpretable classification algorithms (i.e. three types of Bayesian network classification algorithms and the k-nearest neighbours classification algorithm). Moreover, this book discusses the application of those hierarchical feature selection algorithms on the well-known Gene Ontology database, where the entries (terms) are hierarchically structured. Gene Ontology database that unifies the representations of gene and gene products annotation provides the resource for mining valuable knowledge about certain biological research topics, such as the Biology of Ageing. Furthermore, this book discusses the mined biological patterns by the hierarchical feature selection algorithms relevant to the ageing-associated genes. Those patterns reveal the potential ageing-associated factors that inspire future research directions for the Biology of Ageing research.

Feature Selection for Knowledge Discovery and Data Mining

Author : Huan Liu
Publisher : Springer Science & Business Media
ISBN 13 : 1461556899
Total Pages : 225 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis Feature Selection for Knowledge Discovery and Data Mining by : Huan Liu

Download or read book Feature Selection for Knowledge Discovery and Data Mining written by Huan Liu and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e. g. , machine learning and data mining sys tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its simplest form, raw data are represented in feature-values. The size of a dataset can be measUJ·ed in two dimensions, number of features (N) and number of instances (P). Both Nand P can be enormously large. This enormity may cause serious problems to many data mining systems. Feature selection is one of the long existing methods that deal with these problems. Its objective is to select a minimal subset of features according to some reasonable criteria so that the original task can be achieved equally well, if not better. By choosing a minimal subset offeatures, irrelevant and redundant features are removed according to the criterion. When N is reduced, the data space shrinks and in a sense, the data set is now a better representative of the whole data population. If necessary, the reduction of N can also give rise to the reduction of P by eliminating duplicates.

Data Science Concepts and Techniques with Applications

Author : Usman Qamar
Publisher : Springer Nature
ISBN 13 : 3031174429
Total Pages : 492 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Data Science Concepts and Techniques with Applications by : Usman Qamar

Download or read book Data Science Concepts and Techniques with Applications written by Usman Qamar and published by Springer Nature. This book was released on 2023-04-02 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.

Exploiting Semantic Web Knowledge Graphs in Data Mining

Author : P. Ristoski
Publisher : IOS Press
ISBN 13 : 1614999813
Total Pages : 246 pages
Book Rating : 4.6/5 (149 download)

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Book Synopsis Exploiting Semantic Web Knowledge Graphs in Data Mining by : P. Ristoski

Download or read book Exploiting Semantic Web Knowledge Graphs in Data Mining written by P. Ristoski and published by IOS Press. This book was released on 2019-06-28 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in this field are knowledge intensive and can benefit from additional knowledge from various sources, so many approaches have been proposed that combine Semantic Web data with the data mining and knowledge discovery process. This book, Exploiting Semantic Web Knowledge Graphs in Data Mining, aims to show that Semantic Web knowledge graphs are useful for generating valuable data mining features that can be used in various data mining tasks. In Part I, Mining Semantic Web Knowledge Graphs, the author evaluates unsupervised feature generation strategies from types and relations in knowledge graphs used in different data mining tasks such as classification, regression, and outlier detection. Part II, Semantic Web Knowledge Graphs Embeddings, proposes an approach that circumvents the shortcomings introduced with the approaches in Part I, developing an approach that is able to embed complete Semantic Web knowledge graphs in a low dimensional feature space where each entity and relation in the knowledge graph is represented as a numerical vector. Finally, Part III, Applications of Semantic Web Knowledge Graphs, describes a list of applications that exploit Semantic Web knowledge graphs like classification and regression, showing that the approaches developed in Part I and Part II can be used in applications in various domains. The book will be of interest to all those working in the field of data mining and KDD.

Hierarchical Feature Selection for Knowledge Discovery

Author : Cen Wan
Publisher :
ISBN 13 : 9783319979205
Total Pages : pages
Book Rating : 4.9/5 (792 download)

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Book Synopsis Hierarchical Feature Selection for Knowledge Discovery by : Cen Wan

Download or read book Hierarchical Feature Selection for Knowledge Discovery written by Cen Wan and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties of this book are three-fold. To begin with, this book discusses the hierarchical feature selection in depth, which is generally a novel research area in Data Mining/Machine Learning. Seven different state-of-the-art hierarchical feature selection algorithms are discussed and evaluated by working with four types of interpretable classification algorithms (i.e. three types of Bayesian network classification algorithms and the k-nearest neighbours classification algorithm). Moreover, this book discusses the application of those hierarchical feature selection algorithms on the well-known Gene Ontology database, where the entries (terms) are hierarchically structured. Gene Ontology database that unifies the representations of gene and gene products annotation provides the resource for mining valuable knowledge about certain biological research topics, such as the Biology of Ageing. Furthermore, this book discusses the mined biological patterns by the hierarchical feature selection algorithms relevant to the ageing-associated genes. Those patterns reveal the potential ageing-associated factors that inspire future research directions for the Biology of Ageing research.

Advances in Knowledge Discovery and Data Mining

Author : De-Nian Yang
Publisher : Springer Nature
ISBN 13 : 9819722624
Total Pages : 431 pages
Book Rating : 4.8/5 (197 download)

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Book Synopsis Advances in Knowledge Discovery and Data Mining by : De-Nian Yang

Download or read book Advances in Knowledge Discovery and Data Mining written by De-Nian Yang and published by Springer Nature. This book was released on with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Computational Methods of Feature Selection

Author : Huan Liu
Publisher : CRC Press
ISBN 13 : 1584888792
Total Pages : 437 pages
Book Rating : 4.5/5 (848 download)

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Book Synopsis Computational Methods of Feature Selection by : Huan Liu

Download or read book Computational Methods of Feature Selection written by Huan Liu and published by CRC Press. This book was released on 2007-10-29 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery. Highlighting current research issues, Computational Methods of Feature Selection introduces the

Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques

Author : Xiaofei He
Publisher : Springer
ISBN 13 : 3319238620
Total Pages : 644 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques by : Xiaofei He

Download or read book Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques written by Xiaofei He and published by Springer. This book was released on 2015-10-13 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 9242 + 9243 constitutes the proceedings of the 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015, held in Suzhou, China, in June 2015. The total of 126 papers presented in the proceedings was carefully reviewed and selected from 416 submissions. They deal with big data, neural networks, image processing, computer vision, pattern recognition and graphics, object detection, dimensionality reduction and manifold learning, unsupervised learning and clustering, anomaly detection, semi-supervised learning.

Large Scale Hierarchical Classification: State of the Art

Author : Azad Naik
Publisher : Springer
ISBN 13 : 303001620X
Total Pages : 104 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Large Scale Hierarchical Classification: State of the Art by : Azad Naik

Download or read book Large Scale Hierarchical Classification: State of the Art written by Azad Naik and published by Springer. This book was released on 2018-10-09 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief covers the technical material related to large scale hierarchical classification (LSHC). HC is an important machine learning problem that has been researched and explored extensively in the past few years. In this book, the authors provide a comprehensive overview of various state-of-the-art existing methods and algorithms that were developed to solve the HC problem in large scale domains. Several challenges faced by LSHC is discussed in detail such as: 1. High imbalance between classes at different levels of the hierarchy 2. Incorporating relationships during model learning leads to optimization issues 3. Feature selection 4. Scalability due to large number of examples, features and classes 5. Hierarchical inconsistencies 6. Error propagation due to multiple decisions involved in making predictions for top-down methods The brief also demonstrates how multiple hierarchies can be leveraged for improving the HC performance using different Multi-Task Learning (MTL) frameworks. The purpose of this book is two-fold: 1. Help novice researchers/beginners to get up to speed by providing a comprehensive overview of several existing techniques. 2. Provide several research directions that have not yet been explored extensively to advance the research boundaries in HC. New approaches discussed in this book include detailed information corresponding to the hierarchical inconsistencies, multi-task learning and feature selection for HC. Its results are highly competitive with the state-of-the-art approaches in the literature.

Metaheuristics in Machine Learning: Theory and Applications

Author : Diego Oliva
Publisher : Springer Nature
ISBN 13 : 3030705420
Total Pages : 765 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Metaheuristics in Machine Learning: Theory and Applications by : Diego Oliva

Download or read book Metaheuristics in Machine Learning: Theory and Applications written by Diego Oliva and published by Springer Nature. This book was released on with total page 765 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.

Spectral Feature Selection for Data Mining

Author : Zheng Alan Zhao
Publisher : CRC Press
ISBN 13 : 1439862109
Total Pages : 220 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Spectral Feature Selection for Data Mining by : Zheng Alan Zhao

Download or read book Spectral Feature Selection for Data Mining written by Zheng Alan Zhao and published by CRC Press. This book was released on 2011-12-14 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervise

Advances in Knowledge Discovery and Data Mining

Author : Joshua Zhexue Huang
Publisher : Springer Science & Business Media
ISBN 13 : 3642208401
Total Pages : 588 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Advances in Knowledge Discovery and Data Mining by : Joshua Zhexue Huang

Download or read book Advances in Knowledge Discovery and Data Mining written by Joshua Zhexue Huang and published by Springer Science & Business Media. This book was released on 2011-05-09 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 6634 and 6635 constitutes the refereed proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011, held in Shenzhen, China in May 2011. The total of 32 revised full papers and 58 revised short papers were carefully reviewed and selected from 331 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, machine learning, artificial intelligence and pattern recognition, data warehousing and databases, statistics, knoweldge engineering, behavior sciences, visualization, and emerging areas such as social network analysis.

Power Transformer Online Monitoring Using Electromagnetic Waves

Author : Gevork B. Gharehpetian
Publisher : Academic Press
ISBN 13 : 0128228024
Total Pages : 338 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Power Transformer Online Monitoring Using Electromagnetic Waves by : Gevork B. Gharehpetian

Download or read book Power Transformer Online Monitoring Using Electromagnetic Waves written by Gevork B. Gharehpetian and published by Academic Press. This book was released on 2023-02-09 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Power Transformer Online Monitoring using Electromagnetic Waves explores how to use electromagnetic wave technology and remote monitoring systems to predict and localize costly mechanical defects and partial discharge challenges in high voltage transformer windings. This innovative approach brings several potential benefits compared with conventional techniques such as frequency response analysis, including impermeability to ambient noise, and online implementation capability. This book reviews both fundamental and state-of-the-art information about all key aspects of condition monitoring using electromagnetic waves. It addresses the simulation of power transformers in CST environment while also explaining the theoretical background of boundary conditions used. Chapters review how to achieve practical online implementation, reliable diagnosis, asset management and remnant life estimation. Partial discharge detection is also discussed. - Discusses the advantages and disadvantages of the electromagnetic wave method in comparison with classical monitoring methods - Explores how to design and implement power transformer monitoring systems using electromagnetic waves - Investigates partial discharge detection and localization in addition to the partial discharge emission effects on defect detection

Embedding Knowledge Graphs with RDF2vec

Author : Heiko Paulheim
Publisher : Springer Nature
ISBN 13 : 3031303873
Total Pages : 165 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Embedding Knowledge Graphs with RDF2vec by : Heiko Paulheim

Download or read book Embedding Knowledge Graphs with RDF2vec written by Heiko Paulheim and published by Springer Nature. This book was released on 2023-06-03 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to compute vector representations from a graph, known as RDF2vec. The authors describe its usage in practice, from reusing pre-trained knowledge graph embeddings to training tailored vectors for a knowledge graph at hand. They also demonstrate different extensions of RDF2vec and how they affect not only the downstream performance, but also the expressivity of the resulting vector representation, and analyze the resulting vector spaces and the semantic properties they encode.

Data Management, Analytics and Innovation

Author : Neha Sharma
Publisher : Springer Nature
ISBN 13 : 981162934X
Total Pages : 460 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Data Management, Analytics and Innovation by : Neha Sharma

Download or read book Data Management, Analytics and Innovation written by Neha Sharma and published by Springer Nature. This book was released on 2021-08-04 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest findings in the areas of data management and smart computing, machine learning, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at Fifth International Conference on Data Management, Analytics and Innovation (ICDMAI 2021), held during January 15–17, 2021, in a virtual mode. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.

Machine Learning and Knowledge Discovery in Databases, Part III

Author : Dimitrios Gunopulos
Publisher : Springer Science & Business Media
ISBN 13 : 3642238076
Total Pages : 683 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Machine Learning and Knowledge Discovery in Databases, Part III by : Dimitrios Gunopulos

Download or read book Machine Learning and Knowledge Discovery in Databases, Part III written by Dimitrios Gunopulos and published by Springer Science & Business Media. This book was released on 2011-09-06 with total page 683 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.

Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science

Author : Panda, Mrutyunjaya
Publisher : IGI Global
ISBN 13 : 1799866610
Total Pages : 392 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science by : Panda, Mrutyunjaya

Download or read book Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science written by Panda, Mrutyunjaya and published by IGI Global. This book was released on 2021-01-08 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.