Automatic Design Of Decision Tree Induction Algorithms

Author : Rodrigo C. Barros,André C.P.L.F de Carvalho,Alex A. Freitas
Publisher : Springer
Page : 184 pages
File Size : 51,5 Mb
Release : 2015-02-04
Category : Computers
ISBN : 9783319142319

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Automatic Design of Decision-Tree Induction Algorithms by Rodrigo C. Barros,André C.P.L.F de Carvalho,Alex A. Freitas Pdf

Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics. "Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike.

Author : Nelishia Pillay,Rong Qu
Publisher : Springer Nature
Page : 187 pages
File Size : 55,8 Mb
Release : 2021-07-28
Category : Computers
ISBN : 9783030720698

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Automated Design of Machine Learning and Search Algorithms by Nelishia Pillay,Rong Qu Pdf

This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical techniques, machine learning and hyper-heuristics. The book first defines the field of automated design, distinguishing it from the similar but different topics of automated algorithm configuration and automated algorithm selection. The chapters report on the current state of the art by experts in the field and include reviews of AutoML and automated design of search, theoretical analyses of automated algorithm design, automated design of control software for robot swarms, and overfitting as a benchmark and design tool. Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms. The book concludes by examining future research directions of this rapidly evolving field. The information presented here will especially interest researchers and practitioners in the fields of artificial intelligence, computational intelligence, evolutionary computation and optimisation.

Author : Gisele L. Pappa,Alex Freitas
Publisher : Springer Science & Business Media
Page : 198 pages
File Size : 43,5 Mb
Release : 2009-10-27
Category : Computers
ISBN : 9783642025419

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Automating the Design of Data Mining Algorithms by Gisele L. Pappa,Alex Freitas Pdf

Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.

Author : Krzysztof Grąbczewski
Publisher : Springer
Page : 349 pages
File Size : 47,5 Mb
Release : 2013-09-11
Category : Technology & Engineering
ISBN : 9783319009605

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Meta-Learning in Decision Tree Induction by Krzysztof Grąbczewski Pdf

The book focuses on different variants of decision tree induction but also describes the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimental methodology and evaluation framework is provided. Meta-learning is discussed in great detail in the second half of the book. The exposition starts by presenting a comprehensive review of many meta-learning approaches explored in the past described in literature, including for instance approaches that provide a ranking of algorithms. The approach described can be related to other work that exploits planning whose aim is to construct data mining workflows. The book stimulates interchange of ideas between different, albeit related, approaches.

Author : Guanglu Sun,Shuai Liu
Publisher : Springer
Page : 557 pages
File Size : 44,9 Mb
Release : 2018-02-01
Category : Computers
ISBN : 9783319733173

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Advanced Hybrid Information Processing by Guanglu Sun,Shuai Liu Pdf

This book constitutes the refereed proceedings of the First International Conference on Advanced Hybrid Information Processing, ADHIB 2017, held in Harbin, China, in July 2017. The 64 full papers were selected from 134 submissions and focus on advanced methods and applications for hybrid information processing.

Author : Khosrow-Pour, D.B.A., Mehdi
Publisher : IGI Global
Page : 1482 pages
File Size : 41,9 Mb
Release : 2018-09-14
Category : Business & Economics
ISBN : 9781522573630

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Advanced Methodologies and Technologies in Business Operations and Management by Khosrow-Pour, D.B.A., Mehdi Pdf

Businesses consistently work on new projects, products, and workflows to remain competitive and successful in the modern business environment. To remain zealous, businesses must employ the most effective methods and tools in human resources, project management, and overall business plan execution as competitors work to succeed as well. Advanced Methodologies and Technologies in Business Operations and Management provides emerging research on business tools such as employee engagement, payout policies, and financial investing to promote operational success. While highlighting the challenges facing modern organizations, readers will learn how corporate social responsibility and utilizing artificial intelligence improve a company’s culture and management. This book is an ideal resource for executives and managers, researchers, accountants, and financial investors seeking current research on business operations and management.

Author : Khosrow-Pour, D.B.A., Mehdi
Publisher : IGI Global
Page : 8104 pages
File Size : 53,7 Mb
Release : 2017-06-20
Category : Computers
ISBN : 9781522522560

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Encyclopedia of Information Science and Technology, Fourth Edition by Khosrow-Pour, D.B.A., Mehdi Pdf

In recent years, our world has experienced a profound shift and progression in available computing and knowledge sharing innovations. These emerging advancements have developed at a rapid pace, disseminating into and affecting numerous aspects of contemporary society. This has created a pivotal need for an innovative compendium encompassing the latest trends, concepts, and issues surrounding this relevant discipline area. During the past 15 years, the Encyclopedia of Information Science and Technology has become recognized as one of the landmark sources of the latest knowledge and discoveries in this discipline. The Encyclopedia of Information Science and Technology, Fourth Edition is a 10-volume set which includes 705 original and previously unpublished research articles covering a full range of perspectives, applications, and techniques contributed by thousands of experts and researchers from around the globe. This authoritative encyclopedia is an all-encompassing, well-established reference source that is ideally designed to disseminate the most forward-thinking and diverse research findings. With critical perspectives on the impact of information science management and new technologies in modern settings, including but not limited to computer science, education, healthcare, government, engineering, business, and natural and physical sciences, it is a pivotal and relevant source of knowledge that will benefit every professional within the field of information science and technology and is an invaluable addition to every academic and corporate library.

Author : Yeliz Karaca,Carlo Cattani
Publisher : Walter de Gruyter GmbH & Co KG
Page : 395 pages
File Size : 49,5 Mb
Release : 2018-12-17
Category : Mathematics
ISBN : 9783110496369

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Computational Methods for Data Analysis by Yeliz Karaca,Carlo Cattani Pdf

This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.

Author : Krzysztof Gr Bczewski
Publisher : Unknown
Page : 360 pages
File Size : 54,7 Mb
Release : 2013-09-30
Category : Electronic
ISBN : 3319009613

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Meta-Learning in Decision Tree Induction by Krzysztof Gr Bczewski Pdf

Author : Lior Rokach,Oded Z. Maimon
Publisher : World Scientific
Page : 263 pages
File Size : 40,5 Mb
Release : 2008
Category : Computers
ISBN : 9789812771711

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Data Mining with Decision Trees by Lior Rokach,Oded Z. Maimon Pdf

This is the first comprehensive book dedicated entirely to the field of decision trees in data mining and covers all aspects of this important technique.Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition. This book invites readers to explore the many benefits in data mining that decision trees offer: Self-explanatory and easy to follow when compacted Able to handle a variety of input data: nominal, numeric and textual Able to process datasets that may have errors or missing values High predictive performance for a relatively small computational effort Available in many data mining packages over a variety of platforms Useful for various tasks, such as classification, regression, clustering and feature selection

Author : Katharina Morik,Peter Marwedel
Publisher : Walter de Gruyter GmbH & Co KG
Page : 542 pages
File Size : 45,8 Mb
Release : 2022-12-31
Category : Science
ISBN : 9783110786125

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Machine Learning under Resource Constraints - Fundamentals by Katharina Morik,Peter Marwedel Pdf

Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.

Author : Kaveh Sheibani
Publisher : ORLAB Analytics
Page : 31 pages
File Size : 53,5 Mb
Release : 2020-12-31
Category : Business & Economics
ISBN : 8210379456XXX

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Lecture Notes in Management Science by Kaveh Sheibani Pdf

We are pleased to welcome readers to this issue of Lecture Notes in Management Science (LNMS), Volume 11. The series reports significant scientific research results in the field of operational research and management science (OR/MS). The variety of material published usually includes proceedings or post-proceedings for respective conferences, monographs and technical reports which may be based on outstanding research projects.

Author : Rakesh M. Verma,David J. Marchette
Publisher : CRC Press
Page : 431 pages
File Size : 41,7 Mb
Release : 2019-11-25
Category : Mathematics
ISBN : 9781000727890

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Cybersecurity Analytics by Rakesh M. Verma,David J. Marchette Pdf

Cybersecurity Analytics is for the cybersecurity student and professional who wants to learn data science techniques critical for tackling cybersecurity challenges, and for the data science student and professional who wants to learn about cybersecurity adaptations. Trying to build a malware detector, a phishing email detector, or just interested in finding patterns in your datasets? This book can let you do it on your own. Numerous examples and datasets links are included so that the reader can "learn by doing." Anyone with a basic college-level calculus course and some probability knowledge can easily understand most of the material. The book includes chapters containing: unsupervised learning, semi-supervised learning, supervised learning, text mining, natural language processing, and more. It also includes background on security, statistics, and linear algebra. The website for the book contains a listing of datasets, updates, and other resources for serious practitioners.

Author : Yufeng Wang,Athanasios V. Vasilakos,Qun Jin,Hongbo Zhu
Publisher : CRC Press
Page : 478 pages
File Size : 40,5 Mb
Release : 2017-07-12
Category : Computers
ISBN : 9781498724180

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Device-to-Device based Proximity Service by Yufeng Wang,Athanasios V. Vasilakos,Qun Jin,Hongbo Zhu Pdf

D2D-based proximity service is a very hot topic with great commercial potential from an application standpoint. Unlike existing books which focus on D2D communications technologies, this book fills a gap by summarizing and analyzing the latest applications and research results in academic, industrial fields, and standardization. The authors present the architecture, fundamental issues, and applications in a D2D networking environment from both application and interdisciplinary points of view.

Author : Giovanni Squillero,Kevin Sim
Publisher : Springer
Page : 243 pages
File Size : 44,9 Mb
Release : 2017-04-03
Category : Computers
ISBN : 9783319557922

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Applications of Evolutionary Computation by Giovanni Squillero,Kevin Sim Pdf

The two volumes LNCS 10199 and 10200 constitute the refereed conference proceedings of the 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017, held in Amsterdam, The Netherlands, in April 2017, colocated with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented together with 26 poster papers were carefully reviewed and selected from 108 submissions. EvoApplications 2016 consisted of the following 13 tracks: EvoBAFIN (natural computing methods in business analytics and finance), EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoKNOW (knowledge incorporation in evolutionary computation), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoROBOT (evolutionary robotics), EvoSET (nature-inspired algorithms in software engineering and testing), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).