Feature Extraction Construction And Selection

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Feature Extraction, Construction and Selection

Huan Liu,Hiroshi Motoda
Feature Extraction Construction and Selection Book PDF

Author : Huan Liu,Hiroshi Motoda
Publisher : Springer Science & Business Media
Page : 418 pages
File Size : 50,5 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461557258

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Feature Extraction, Construction and Selection by Huan Liu,Hiroshi Motoda Pdf

There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today's advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem.

Feature Extraction

Isabelle Guyon,Steve Gunn,Masoud Nikravesh,Lofti A. Zadeh
Feature Extraction Book PDF

Author : Isabelle Guyon,Steve Gunn,Masoud Nikravesh,Lofti A. Zadeh
Publisher : Springer
Page : 778 pages
File Size : 42,8 Mb
Release : 2008-11-16
Category : Computers
ISBN : 9783540354888

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Feature Extraction by Isabelle Guyon,Steve Gunn,Masoud Nikravesh,Lofti A. Zadeh Pdf

This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. Until now there has been insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons.

Computational Methods of Feature Selection

Huan Liu,Hiroshi Motoda
Computational Methods of Feature Selection Book PDF

Author : Huan Liu,Hiroshi Motoda
Publisher : CRC Press
Page : 440 pages
File Size : 54,8 Mb
Release : 2007-10-29
Category : Computers
ISBN : 1584888792

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Computational Methods of Feature Selection by Huan Liu,Hiroshi Motoda Pdf

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 basic concepts and principles, state-of-the-art algorithms, and novel applications of this tool. The book begins by exploring unsupervised, randomized, and causal feature selection. It then reports on some recent results of empowering feature selection, including active feature selection, decision-border estimate, the use of ensembles with independent probes, and incremental feature selection. This is followed by discussions of weighting and local methods, such as the ReliefF family, k-means clustering, local feature relevance, and a new interpretation of Relief. The book subsequently covers text classification, a new feature selection score, and both constraint-guided and aggressive feature selection. The final section examines applications of feature selection in bioinformatics, including feature construction as well as redundancy-, ensemble-, and penalty-based feature selection. Through a clear, concise, and coherent presentation of topics, this volume systematically covers the key concepts, underlying principles, and inventive applications of feature selection, illustrating how this powerful tool can efficiently harness massive, high-dimensional data and turn it into valuable, reliable information.

Advances in Artificial Intelligence

Balázs Kégl,Guy Lapalme
Advances in Artificial Intelligence Book PDF

Author : Balázs Kégl,Guy Lapalme
Publisher : Springer
Page : 458 pages
File Size : 48,9 Mb
Release : 2005-05-03
Category : Computers
ISBN : 9783540319528

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Advances in Artificial Intelligence by Balázs Kégl,Guy Lapalme Pdf

The 18th conference of the Canadian Society for the Computational Study of Intelligence (CSCSI) continued the success of its predecessors. This set of - pers re?ects the diversity of the Canadian AI community and its international partners. AI 2005 attracted 135 high-quality submissions: 64 from Canada and 71 from around the world. Of these, eight were written in French. All submitted papers were thoroughly reviewed by at least three members of the Program Committee. A total of 30 contributions, accepted as long papers, and 19 as short papers are included in this volume. We invited three distinguished researchers to give talks about their current research interests: Eric Brill from Microsoft Research, Craig Boutilier from the University of Toronto, and Henry Krautz from the University of Washington. The organization of such a successful conference bene?ted from the coll- oration of many individuals. Foremost, we would like to express our apprec- tion to the Program Committee members and external referees, who provided timely and signi?cant reviews. To manage the submission and reviewing process we used the Paperdyne system, which was developed by Dirk Peters. We owe special thanks to Kellogg Booth and Tricia d’Entremont for handling the local arrangementsandregistration.WealsothankBruceSpencerandmembersofthe CSCSI executive for all their e?orts in making AI 2005 a successful conference.

Feature Selection for Knowledge Discovery and Data Mining

Huan Liu,Hiroshi Motoda
Feature Selection for Knowledge Discovery and Data Mining Book PDF

Author : Huan Liu,Hiroshi Motoda
Publisher : Springer Science & Business Media
Page : 225 pages
File Size : 48,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461556893

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Feature Selection for Knowledge Discovery and Data Mining by Huan Liu,Hiroshi Motoda Pdf

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.

Feature Selection for Data and Pattern Recognition

Urszula Stańczyk,Lakhmi C. Jain
Feature Selection for Data and Pattern Recognition Book PDF

Author : Urszula Stańczyk,Lakhmi C. Jain
Publisher : Springer
Page : 0 pages
File Size : 54,7 Mb
Release : 2016-09-24
Category : Technology & Engineering
ISBN : 3662508451

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Feature Selection for Data and Pattern Recognition by Urszula Stańczyk,Lakhmi C. Jain Pdf

This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition. Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks. This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies.

Feature Engineering for Machine Learning and Data Analytics

Guozhu Dong,Huan Liu
Feature Engineering for Machine Learning and Data Analytics Book PDF

Author : Guozhu Dong,Huan Liu
Publisher : CRC Press
Page : 400 pages
File Size : 41,8 Mb
Release : 2018-03-14
Category : Business & Economics
ISBN : 9781351721271

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Feature Engineering for Machine Learning and Data Analytics by Guozhu Dong,Huan Liu Pdf

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.

Feature Engineering for Machine Learning

Alice Zheng,Amanda Casari
Feature Engineering for Machine Learning Book PDF

Author : Alice Zheng,Amanda Casari
Publisher : "O'Reilly Media, Inc."
Page : 218 pages
File Size : 43,8 Mb
Release : 2018-03-23
Category : Computers
ISBN : 9781491953198

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Feature Engineering for Machine Learning by Alice Zheng,Amanda Casari Pdf

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques

Lazy Learning

David W. Aha
Lazy Learning Book PDF

Author : David W. Aha
Publisher : Springer Science & Business Media
Page : 421 pages
File Size : 42,7 Mb
Release : 2013-06-29
Category : Computers
ISBN : 9789401720533

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Lazy Learning by David W. Aha Pdf

This edited collection describes recent progress on lazy learning, a branch of machine learning concerning algorithms that defer the processing of their inputs, reply to information requests by combining stored data, and typically discard constructed replies. It is the first edited volume in AI on this topic, whose many synonyms include `instance-based', `memory-based'. `exemplar-based', and `local learning', and whose topic intersects case-based reasoning and edited k-nearest neighbor classifiers. It is intended for AI researchers and students interested in pursuing recent progress in this branch of machine learning, but, due to the breadth of its contributions, it should also interest researchers and practitioners of data mining, case-based reasoning, statistics, and pattern recognition.

Applied Text Analysis with Python

Benjamin Bengfort,Rebecca Bilbro,Tony Ojeda
Applied Text Analysis with Python Book PDF

Author : Benjamin Bengfort,Rebecca Bilbro,Tony Ojeda
Publisher : "O'Reilly Media, Inc."
Page : 332 pages
File Size : 43,6 Mb
Release : 2018-06-11
Category : Computers
ISBN : 9781491962992

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Applied Text Analysis with Python by Benjamin Bengfort,Rebecca Bilbro,Tony Ojeda Pdf

From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity

Spectral Feature Selection for Data Mining (Open Access)

Zheng Alan Zhao,Huan Liu
Spectral Feature Selection for Data Mining Open Access Book PDF

Author : Zheng Alan Zhao,Huan Liu
Publisher : CRC Press
Page : 224 pages
File Size : 48,8 Mb
Release : 2011-12-14
Category : Business & Economics
ISBN : 9781439862100

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Spectral Feature Selection for Data Mining (Open Access) by Zheng Alan Zhao,Huan Liu Pdf

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

Feature Engineering and Selection

Max Kuhn,Kjell Johnson
Feature Engineering and Selection Book PDF

Author : Max Kuhn,Kjell Johnson
Publisher : CRC Press
Page : 266 pages
File Size : 45,9 Mb
Release : 2019-07-25
Category : Business & Economics
ISBN : 9781351609463

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Feature Engineering and Selection by Max Kuhn,Kjell Johnson Pdf

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.

Feature Engineering Made Easy

Sinan Ozdemir,Divya Susarla
Feature Engineering Made Easy Book PDF

Author : Sinan Ozdemir,Divya Susarla
Publisher : Packt Publishing Ltd
Page : 310 pages
File Size : 41,5 Mb
Release : 2018-01-22
Category : Computers
ISBN : 9781787286474

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Feature Engineering Made Easy by Sinan Ozdemir,Divya Susarla Pdf

A perfect guide to speed up the predicting power of machine learning algorithms Key Features Design, discover, and create dynamic, efficient features for your machine learning application Understand your data in-depth and derive astonishing data insights with the help of this Guide Grasp powerful feature-engineering techniques and build machine learning systems Book Description Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your data—often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization. What you will learn Identify and leverage different feature types Clean features in data to improve predictive power Understand why and how to perform feature selection, and model error analysis Leverage domain knowledge to construct new features Deliver features based on mathematical insights Use machine-learning algorithms to construct features Master feature engineering and optimization Harness feature engineering for real world applications through a structured case study Who this book is for If you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of the machine learning concepts and Python scripting would be enough to get started with this book.

The Art of Feature Engineering

Pablo Duboue
The Art of Feature Engineering Book PDF

Author : Pablo Duboue
Publisher : Cambridge University Press
Page : 287 pages
File Size : 45,9 Mb
Release : 2020-06-25
Category : Computers
ISBN : 9781108709385

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The Art of Feature Engineering by Pablo Duboue Pdf

A practical guide for data scientists who want to improve the performance of any machine learning solution with feature engineering.

Research and Development in Intelligent Systems XXI

Frans Coenen,Tony Allen
Research and Development in Intelligent Systems XXI Book PDF

Author : Frans Coenen,Tony Allen
Publisher : Springer Science & Business Media
Page : 343 pages
File Size : 54,8 Mb
Release : 2007-12-24
Category : Computers
ISBN : 9781846281020

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Research and Development in Intelligent Systems XXI by Frans Coenen,Tony Allen Pdf

The refereed technical papers in this volume present new and innovative developments in this important field; essential reading for those who wish to keep up to date on intelligent systems.