Automating The Design Of Data Mining Algorithms
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Automating The Design Of Data Mining Algorithms
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Author : Gisele L. Pappa
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-10-27
Automating The Design Of Data Mining Algorithms written by Gisele L. Pappa and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-10-27 with Computers categories.
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.
Automated Machine Learning In Action
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Author : Qingquan Song
language : en
Publisher: Simon and Schuster
Release Date : 2022-06-07
Automated Machine Learning In Action written by Qingquan Song and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-07 with Computers categories.
Automated Machine Learning in Action reveals how you can automate the burdensome elements of designing and tuning your machine learning systems. --
Role Mining In Business Taming Role Based Access Control Administration
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Author : Roberto Di Pietro
language : en
Publisher: World Scientific
Release Date : 2012-02-20
Role Mining In Business Taming Role Based Access Control Administration written by Roberto Di Pietro and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-20 with Computers categories.
With continuous growth in the number of information objects and the users that can access these objects, ensuring that access is compliant with company policies has become a big challenge. Role-based Access Control (RBAC) — a policy-neutral access control model that serves as a bridge between academia and industry — is probably the most suitable security model for commercial applications.Interestingly, role design determines RBAC's cost. When there are hundreds or thousands of users within an organization, with individual functions and responsibilities to be accurately reflected in terms of access permissions, only a well-defined role engineering process allows for significant savings of time and money while protecting data and systems.Among role engineering approaches, searching through access control systems to find de facto roles embedded in existing permissions is attracting increasing interest. The focus falls on role mining, which is applied data mining techniques to automate — to the extent possible — the role design task.This book explores existing role mining algorithms and offers insights into the automated role design approaches proposed in the literature. Alongside theory, this book acts as a practical guide for using role mining tools when implementing RBAC. Besides a comprehensive survey of role mining techniques deeply rooted in academic research, this book also provides a summary of the role-based approach, access control concepts and describes a typical role engineering process.Among the pioneering works on role mining, this book blends business elements with data mining theory, and thus further extends the applications of role mining into business practice. This makes it a useful guide for all academics, IT and business professionals.
Algorithms And Applications For Academic Search Recommendation And Quantitative Association Rule Mining
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Author : Emmanouil Amolochitis
language : en
Publisher: CRC Press
Release Date : 2022-09-01
Algorithms And Applications For Academic Search Recommendation And Quantitative Association Rule Mining written by Emmanouil Amolochitis and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-01 with Technology & Engineering categories.
Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining presents novel algorithms for academic search, recommendation and association rule mining that have been developed and optimized for different commercial as well as academic purpose systems. Along with the design and implementation of algorithms, a major part of the work presented in the book involves the development of new systems both for commercial as well as for academic use. In the first part of the book the author introduces a novel hierarchical heuristic scheme for re-ranking academic publications retrieved from standard digital libraries. The scheme is based on the hierarchical combination of a custom implementation of the term frequency heuristic, a time-depreciated citation score and a graph-theoretic computed score that relates the paper's index terms with each other. In order to evaluate the performance of the introduced algorithms, a meta-search engine has been designed and developed that submits user queries to standard digital repositories of academic publications and re-ranks the top-n results using the introduced hierarchical heuristic scheme. In the second part of the book the design of novel recommendation algorithms with application in different types of e-commerce systems are described. The newly introduced algorithms are a part of a developed Movie Recommendation system, the first such system to be commercially deployed in Greece by a major Triple Play services provider. The initial version of the system uses a novel hybrid recommender (user, item and content based) and provides daily recommendations to all active subscribers of the provider (currently more than 30,000). The recommenders that we are presenting are hybrid by nature, using an ensemble configuration of different content, user as well as item-based recommenders in order to provide more accurate recommendation results. The final part of the book presents the design of a quantitative association rule mining algorithm. Quantitative association rules refer to a special type of association rules of the form that antecedent implies consequent consisting of a set of numerical or quantitative attributes. The introduced mining algorithm processes a specific number of user histories in order to generate a set of association rules with a minimally required support and confidence value. The generated rules show strong relationships that exist between the consequent and the antecedent of each rule, representing different items that have been consumed at specific price levels. This research book will be of appeal to researchers, graduate students, professionals, engineers and computer programmers.
Bioinformatics Applications Based On Machine Learning
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Author : Pablo Chamoso
language : en
Publisher: MDPI
Release Date : 2021-09-01
Bioinformatics Applications Based On Machine Learning written by Pablo Chamoso and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-01 with Technology & Engineering categories.
The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems.
Intelligent Data Engineering And Automated Learning Ideal 2012
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Author : Hujun Yin
language : en
Publisher: Springer
Release Date : 2012-08-01
Intelligent Data Engineering And Automated Learning Ideal 2012 written by Hujun Yin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-01 with Computers categories.
This book constitutes the refereed proceedings of the 13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012, held in Natal, Brazil, in August 2012. The 100 revised full papers presented were carefully reviewed and selected from more than 200 submissions for inclusion in the book and present the latest theoretical advances and real-world applications in computational intelligence.
Evolutionary Multi Objective System Design
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Author : Nadia Nedjah
language : en
Publisher: CRC Press
Release Date : 2020-07-15
Evolutionary Multi Objective System Design written by Nadia Nedjah and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-15 with Computers categories.
Real-world engineering problems often require concurrent optimization of several design objectives, which are conflicting in cases. This type of optimization is generally called multi-objective or multi-criterion optimization. The area of research that applies evolutionary methodologies to multi-objective optimization is of special and growing interest. It brings a viable computational solution to many real-world problems. Generally, multi-objective engineering problems do not have a straightforward optimal design. These kinds of problems usually inspire several solutions of equal efficiency, which achieve different trade-offs. Decision makers’ preferences are normally used to select the most adequate design. Such preferences may be dictated before or after the optimization takes place. They may also be introduced interactively at different levels of the optimization process. Multi-objective optimization methods can be subdivided into classical and evolutionary. The classical methods usually aim at a single solution while the evolutionary methods provide a whole set of so-called Pareto-optimal solutions. Evolutionary Multi-Objective System Design: Theory and Applications provides a representation of the state-of-the-art in evolutionary multi-objective optimization research area and related new trends. It reports many innovative designs yielded by the application of such optimization methods. It also presents the application of multi-objective optimization to the following problems: Embrittlement of stainless steel coated electrodes Learning fuzzy rules from imbalanced datasets Combining multi-objective evolutionary algorithms with collective intelligence Fuzzy gain scheduling control Smart placement of roadside units in vehicular networks Combining multi-objective evolutionary algorithms with quasi-simplex local search Design of robust substitution boxes Protein structure prediction problem Core assignment for efficient network-on-chip-based system design
Service Oriented Distributed Knowledge Discovery
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Author : Domenico Talia
language : en
Publisher: CRC Press
Release Date : 2012-10-05
Service Oriented Distributed Knowledge Discovery written by Domenico Talia and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-05 with Computers categories.
A new approach to distributed large-scale data mining, service-oriented knowledge discovery extracts useful knowledge from today’s often unmanageable volumes of data by exploiting data mining and machine learning distributed models and techniques in service-oriented infrastructures. Service-Oriented Distributed Knowledge Discovery presents techniques, algorithms, and systems based on the service-oriented paradigm. Through detailed descriptions of real software systems, it shows how the techniques, models, and architectures can be implemented. The book covers key areas in data mining and service-oriented computing. It presents the concepts and principles of distributed knowledge discovery and service-oriented data mining. The authors illustrate how to design services for data analytics, describe real systems for implementing distributed knowledge discovery applications, and explore mobile data mining models. They also discuss the future role of service-oriented knowledge discovery in ubiquitous discovery processes and large-scale data analytics. Highlighting the latest achievements in the field, the book gives many examples of the state of the art in service-oriented knowledge discovery. Both novices and more seasoned researchers will learn useful concepts related to distributed data mining and service-oriented data analysis. Developers will also gain insight on how to successfully use service-oriented knowledge discovery in databases (KDD) frameworks.
Automated Design Of Machine Learning And Search Algorithms
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Author : Nelishia Pillay
language : en
Publisher: Springer Nature
Release Date : 2021-07-28
Automated Design Of Machine Learning And Search Algorithms written by Nelishia Pillay and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-28 with Computers categories.
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.
Automating The News
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Author : Nicholas Diakopoulos
language : en
Publisher:
Release Date : 2019
Automating The News written by Nicholas Diakopoulos and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Computers categories.
From hidden connections in big data to bots spreading fake news, journalism is increasingly computer-generated. Nicholas Diakopoulos explains the present and future of a world in which algorithms have changed how the news is created, disseminated, and received, and he shows why journalists--and their values--are at little risk of being replaced.