Download or read book Feature Extraction Construction and Selection written by Huan Liu and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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
Download or read book Feature Extraction written by Isabelle Guyon and published by Springer. This book was released on 2008-11-16 with total page 765 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Feature Engineering for Machine Learning written by Alice Zheng and published by "O'Reilly Media, Inc.". This book was released on 2018-03-23 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Advances in Artificial Intelligence written by Balázs Kégl and published by Springer. This book was released on 2005-05-03 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Feature Selection for Data and Pattern Recognition written by Urszula Stańczyk and published by Springer. This book was released on 2016-09-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Lazy Learning written by David W. Aha and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Feature Selection and Extraction written by Swair Rajesh Shah and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Feature selection is a very important process in statistics and machine learning. It removes redundant and irrelevant features and selects the most useful set of features from a given dataset. This tends to improve generalization of machine learning algorithms and reduces training time. Feature selection is used to make the models more interpretable. Recently it has been also used to reduce bias of such models and ensure fairness of the outcome. Feature extraction is another dimensionality reduction process which finds a small set of features to approximate a given dataset. Unlike feature selection in extraction the resulting features can be arbitrary functions of the features in the original dataset. There are fast algorithms to compute feature extraction but it doesn’t provide the interpretability aspect of feature selection and it tends to be less effective than feature selection in making models generalize better. One of the problems addressed in this dissertation is a hybrid problem which combines feature selection and extraction. This hybrid problem is at least as hard as feature selection which is known to be NP-hard. We show how simplistic sequential application of optimal selection and extraction does not provide an optimal solution for this problem. We develop an algorithm to solve the hybrid problem optimally using methods inspired by the classic A* search algorithm. One of the most widely used feature extraction methods is the Principal Component Analysis (PCA). It is known to be very sensitive to the outliers in the data. There have been various attempts in the literature to address this issue none promising an optimal solution to the problem. We model this problem as a graph search problem and again apply our heuristic search framework to design an algorithm which solves this problem optimally. We show that we compare favorably to the state-of-the-art convex relaxation approach. PCA is closely tied to a very popular linear algebra problem called the eigenvalue problem. The third part of the dissertation uses the eigenvalue problem and a variant of it known as the generalized eigenvalue problem to achieve the privacy of the user data. Today there are many companies which provide predictive models as services. In order to use these services one needs to send one’s data to such a service for prediction or inference. It is possible that this data can be used to infer some confidential information about the data sender. We design algorithms to apply transformations to this data so that the inference of the confidential information is prevented while the data can still be used to infer the desired information.
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.
Download or read book Spectral Feature Selection for Data Mining Open Access written by Zheng Alan Zhao and published by CRC Press. This book was released on 2011-12-14 with total page 224 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
Download or read book Feature Engineering Made Easy written by Sinan Ozdemir and published by Packt Publishing Ltd. This book was released on 2018-01-22 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Applied Text Analysis with Python written by Benjamin Bengfort and published by "O'Reilly Media, Inc.". This book was released on 2018-06-11 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book The Art of Feature Engineering written by Pablo Duboue and published by Cambridge University Press. This book was released on 2020-06-25 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide for data scientists who want to improve the performance of any machine learning solution with feature engineering.
Download or read book Research and Development in Intelligent Systems XXI written by Frans Coenen and published by Springer Science & Business Media. This book was released on 2007-12-24 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Feature Selection Techniques for Classification and Clustering written by Ananya Gupta and published by . This book was released on 2023-01-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are several feature selection techniques that can be used for classification and clustering, including: Wrapper methods: These methods use a specific learning algorithm to evaluate the importance of each feature. Examples include forward selection and backward elimination. Filter methods: These methods use a statistical test to evaluate the importance of each feature. Examples include chi-squared test and mutual information. Embedded methods: These methods use a learning algorithm that has built-in feature selection capabilities. Examples include Lasso and Ridge regression in linear models. Hybrid methods: These methods combine the strengths of wrapper and filter methods. Correlation-based feature selection (CFS): This method uses correlation between features and the target variable to select the relevant features. Recursive Feature Elimination (RFE): This method recursively removing attributes and building a model on those attributes that remain. It uses the model accuracy to identify which attributes (and combination of attributes) contribute the most to predicting the target attribute. Overall, the choice of feature selection technique will depend on the specific problem and dataset at hand. The data mining tasks are often confronted with many challenges, biggest being the large dimension of the datasets. For successful data mining, the most important criterion is the dimensionality reduction of the dataset. The problem of dimensionality has imposed a very big challenge towards the efficiency of the data mining algorithms. The data mining algorithms cannot handle these high dimensional data as they render the mining tasks intractable. Thus, it becomes necessary to reduce the dimensionality of the data. There are two methods of dimensionality reduction. They are the feature selection and feature extraction methods (Bishop, 1995, Devijver and Kittler, 1982, Fukunaga, 1990). Feature selection method reduce the dimensionality of the original feature space by selecting a subset of features without any transformation. It preserves the physical interpretability of the selected features as in the original space. Feature extraction method reduce the dimensionality by linear transformation of the input features into a completely different space. The linear transformation involved in feature extraction cause the features to be altered, making their interpretation difficult. Features in the transformed space lose their physical interpretability and their original contribution becomes difficult to ascertain (Bishop, 1995). The choice of the dimensionality reduction method is completely application specific and depends on the nature of the data. Feature selection is advantageous especially as features keep their original physical meaning because no transformation of data is made. This may be important for a better problem understanding in some applications such as text mining and genetic analysis where only relevant information is analysed.
Download or read book Analysis of Feature Extraction and Selection written by Kerry D. LaViolette and published by . This book was released on 1984 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applications of Multi objective Evolutionary Algorithms written by Carlos A. Coello Coello and published by World Scientific. This book was released on 2004 with total page 791 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good solutions for high-dimensional real-world design applications. The book contains a large collection of MOEA applications from many researchers, and thus provides the practitioner with detailed algorithmic direction to achieve good results in their selected problem domain.
