Evolutionary Decision Trees In Large Scale Data Mining

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Evolutionary Decision Trees in Large-Scale Data Mining

Marek Kretowski
Evolutionary Decision Trees in Large Scale Data Mining Book PDF

Author : Marek Kretowski
Publisher : Springer
Page : 180 pages
File Size : 48,9 Mb
Release : 2019-06-05
Category : Computers
ISBN : 9783030218515

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Evolutionary Decision Trees in Large-Scale Data Mining by Marek Kretowski Pdf

This book presents a unified framework, based on specialized evolutionary algorithms, for the global induction of various types of classification and regression trees from data. The resulting univariate or oblique trees are significantly smaller than those produced by standard top-down methods, an aspect that is critical for the interpretation of mined patterns by domain analysts. The approach presented here is extremely flexible and can easily be adapted to specific data mining applications, e.g. cost-sensitive model trees for financial data or multi-test trees for gene expression data. The global induction can be efficiently applied to large-scale data without the need for extraordinary resources. With a simple GPU-based acceleration, datasets composed of millions of instances can be mined in minutes. In the event that the size of the datasets makes the fastest memory computing impossible, the Spark-based implementation on computer clusters, which offers impressive fault tolerance and scalability potential, can be applied.

Data Mining and Knowledge Discovery with Evolutionary Algorithms

Alex A. Freitas
Data Mining and Knowledge Discovery with Evolutionary Algorithms Book PDF

Author : Alex A. Freitas
Publisher : Springer Science & Business Media
Page : 272 pages
File Size : 43,6 Mb
Release : 2013-11-11
Category : Computers
ISBN : 9783662049235

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Data Mining and Knowledge Discovery with Evolutionary Algorithms by Alex A. Freitas Pdf

This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. This book emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making. The text explains both basic concepts and advanced topics

Automating the Design of Data Mining Algorithms

Gisele L. Pappa,Alex Freitas
Automating the Design of Data Mining Algorithms Book PDF

Author : Gisele L. Pappa,Alex Freitas
Publisher : Springer Science & Business Media
Page : 198 pages
File Size : 55,5 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.

Evolutionary Computation in Data Mining

Ashish Ghosh
Evolutionary Computation in Data Mining Book PDF

Author : Ashish Ghosh
Publisher : Springer
Page : 279 pages
File Size : 45,8 Mb
Release : 2006-06-22
Category : Computers
ISBN : 9783540323587

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Evolutionary Computation in Data Mining by Ashish Ghosh Pdf

Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).

Parallel Processing and Applied Mathematics

Roman Wyrzykowski,Jack Dongarra,Ewa Deelman,Konrad Karczewski
Parallel Processing and Applied Mathematics Book PDF

Author : Roman Wyrzykowski,Jack Dongarra,Ewa Deelman,Konrad Karczewski
Publisher : Springer Nature
Page : 487 pages
File Size : 52,7 Mb
Release : 2023-04-27
Category : Computers
ISBN : 9783031304422

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Parallel Processing and Applied Mathematics by Roman Wyrzykowski,Jack Dongarra,Ewa Deelman,Konrad Karczewski Pdf

This two-volume set, LNCS 13826 and LNCS 13827, constitutes the proceedings of the 14th International Conference on Parallel Processing and Applied Mathematics, PPAM 2022, held in Gdansk, Poland, in September 2022. The 77 regular papers presented in these volumes were selected from 132 submissions. For regular tracks of the conference, 33 papers were selected from 62 submissions. The papers were organized in topical sections named as follows: Part I: numerical algorithms and parallel scientific computing; parallel non-numerical algorithms; GPU computing; performance analysis and prediction in HPC systems; scheduling for parallel computing; environments and frameworks for parallel/cloud computing; applications of parallel and distributed computing; soft computing with applications and special session on parallel EVD/SVD and its application in matrix computations. Part II: 9th Workshop on Language-Based Parallel Programming (WLPP 2022); 6th Workshop on Models, Algorithms and Methodologies for Hybrid Parallelism in New HPC Systems (MAMHYP 2022); first workshop on quantum computing and communication; First Workshop on Applications of Machine Learning and Artificial Intelligence in High Performance Computing (WAML 2022); 4th workshop on applied high performance numerical algorithms for PDEs; 5th minisymposium on HPC applications in physical sciences; 8th minisymposium on high performance computing interval methods; 7th workshop on complex collective systems.

Data Mining with Decision Trees

Lior Rokach
Data Mining with Decision Trees Book PDF

Author : Lior Rokach
Publisher : World Scientific
Page : 263 pages
File Size : 44,8 Mb
Release : 2008
Category : Computers
ISBN : 9789812771728

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Data Mining with Decision Trees by Lior Rokach 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 . Sample Chapter(s). Chapter 1: Introduction to Decision Trees (245 KB). Chapter 6: Advanced Decision Trees (409 KB). Chapter 10: Fuzzy Decision Trees (220 KB). Contents: Introduction to Decision Trees; Growing Decision Trees; Evaluation of Classification Trees; Splitting Criteria; Pruning Trees; Advanced Decision Trees; Decision Forests; Incremental Learning of Decision Trees; Feature Selection; Fuzzy Decision Trees; Hybridization of Decision Trees with Other Techniques; Sequence Classification Using Decision Trees. Readership: Researchers, graduate and undergraduate students in information systems, engineering, computer science, statistics and management.

Computational Science – ICCS 2020

Valeria V. Krzhizhanovskaya,Gábor Závodszky,Michael H. Lees,Jack J. Dongarra,Peter M. A. Sloot,Sérgio Brissos,João Teixeira
Computational Science ICCS 2020 Book PDF

Author : Valeria V. Krzhizhanovskaya,Gábor Závodszky,Michael H. Lees,Jack J. Dongarra,Peter M. A. Sloot,Sérgio Brissos,João Teixeira
Publisher : Springer Nature
Page : 648 pages
File Size : 40,5 Mb
Release : 2020-06-19
Category : Computers
ISBN : 9783030504205

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Computational Science – ICCS 2020 by Valeria V. Krzhizhanovskaya,Gábor Závodszky,Michael H. Lees,Jack J. Dongarra,Peter M. A. Sloot,Sérgio Brissos,João Teixeira Pdf

The seven-volume set LNCS 12137, 12138, 12139, 12140, 12141, 12142, and 12143 constitutes the proceedings of the 20th International Conference on Computational Science, ICCS 2020, held in Amsterdam, The Netherlands, in June 2020.* The total of 101 papers and 248 workshop papers presented in this book set were carefully reviewed and selected from 719 submissions (230 submissions to the main track and 489 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track Part III: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Agent-Based Simulations, Adaptive Algorithms and Solvers; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Biomedical and Bioinformatics Challenges for Computer Science Part IV: Classifier Learning from Difficult Data; Complex Social Systems through the Lens of Computational Science; Computational Health; Computational Methods for Emerging Problems in (Dis-)Information Analysis Part V: Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems; Computer Graphics, Image Processing and Artificial Intelligence Part VI: Data Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; Meshfree Methods in Computational Sciences; Multiscale Modelling and Simulation; Quantum Computing Workshop Part VII: Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainties; Teaching Computational Science; UNcErtainty QUantIficatiOn for ComputationAl modeLs *The conference was canceled due to the COVID-19 pandemic.

Artificial Intelligence and Soft Computing

Leszek Rutkowski,Rafał Scherer,Marcin Korytkowski,Witold Pedrycz,Ryszard Tadeusiewicz,Jacek M. Zurada
Artificial Intelligence and Soft Computing Book PDF

Author : Leszek Rutkowski,Rafał Scherer,Marcin Korytkowski,Witold Pedrycz,Ryszard Tadeusiewicz,Jacek M. Zurada
Publisher : Springer
Page : 796 pages
File Size : 51,5 Mb
Release : 2018-05-24
Category : Computers
ISBN : 9783319912530

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Artificial Intelligence and Soft Computing by Leszek Rutkowski,Rafał Scherer,Marcin Korytkowski,Witold Pedrycz,Ryszard Tadeusiewicz,Jacek M. Zurada Pdf

The two-volume set LNAI 10841 and LNAI 10842 constitutes the refereed proceedings of the 17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018, held in Zakopane, Poland in June 2018. The 140 revised full papers presented were carefully reviewed and selected from 242 submissions. The papers included in the first volume are organized in the following three parts: neural networks and their applications; evolutionary algorithms and their applications; and pattern classification.

Parallel Problem Solving from Nature – PPSN XV

Anne Auger,Carlos M. Fonseca,Nuno Lourenço,Penousal Machado,Luís Paquete,Darrell Whitley
Parallel Problem Solving from Nature PPSN XV Book PDF

Author : Anne Auger,Carlos M. Fonseca,Nuno Lourenço,Penousal Machado,Luís Paquete,Darrell Whitley
Publisher : Springer
Page : 501 pages
File Size : 43,9 Mb
Release : 2018-08-30
Category : Computers
ISBN : 9783319992594

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Parallel Problem Solving from Nature – PPSN XV by Anne Auger,Carlos M. Fonseca,Nuno Lourenço,Penousal Machado,Luís Paquete,Darrell Whitley Pdf

This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018, held in Coimbra, Portugal, in September 2018. The 79 revised full papers were carefully reviewed and selected from 205 submissions. The papers cover a wide range of topics in natural computing including evolutionary computation, artificial neural networks, artificial life, swarm intelligence, artificial immune systems, self-organizing systems, emergent behavior, molecular computing, evolutionary robotics, evolvable hardware, parallel implementations and applications to real-world problems. The papers are organized in the following topical sections: numerical optimization; combinatorial optimization; genetic programming; multi-objective optimization; parallel and distributed frameworks; runtime analysis and approximation results; fitness landscape modeling and analysis; algorithm configuration, selection, and benchmarking; machine learning and evolutionary algorithms; and applications. Also included are the descriptions of 23 tutorials and 6 workshops which took place in the framework of PPSN XV.

Theory and Practice of Natural Computing

Carlos Martín-Vide,Roman Neruda,Miguel A. Vega-Rodríguez
Theory and Practice of Natural Computing Book PDF

Author : Carlos Martín-Vide,Roman Neruda,Miguel A. Vega-Rodríguez
Publisher : Springer
Page : 319 pages
File Size : 47,6 Mb
Release : 2017-12-12
Category : Computers
ISBN : 9783319710693

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Theory and Practice of Natural Computing by Carlos Martín-Vide,Roman Neruda,Miguel A. Vega-Rodríguez Pdf

This book constitutes the refereed proceedings of the 6th International Conference,on Theory and Practice of Natural Computing, TPNC 2017, held in Prague, Czech Republic, December 2017. The 22 full papers presented in this book, together with one invited talk, werecarefully reviewed and selected from 39 submissions. The papers are organized around the following topical sections: applications of natural computing; evolutionary computation; fuzzy logic; Molecular computation; neural networks; quantum computing.

Data Mining: A Heuristic Approach

Abbass, Hussein A.,Sarker, Ruhul,Newton, Charles S.
Data Mining A Heuristic Approach Book PDF

Author : Abbass, Hussein A.,Sarker, Ruhul,Newton, Charles S.
Publisher : IGI Global
Page : 310 pages
File Size : 53,5 Mb
Release : 2001-07-01
Category : Computers
ISBN : 9781591400110

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Data Mining: A Heuristic Approach by Abbass, Hussein A.,Sarker, Ruhul,Newton, Charles S. Pdf

Real life problems are known to be messy, dynamic and multi-objective, and involve high levels of uncertainty and constraints. Because traditional problem-solving methods are no longer capable of handling this level of complexity, heuristic search methods have attracted increasing attention in recent years for solving such problems. Inspired by nature, biology, statistical mechanics, physics and neuroscience, heuristics techniques are used to solve many problems where traditional methods have failed. Data Mining: A Heuristic Approach will be a repository for the applications of these techniques in the area of data mining.

Automatic Design of Decision-Tree Induction Algorithms

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

Author : Rodrigo C. Barros,André C.P.L.F de Carvalho,Alex A. Freitas
Publisher : Springer
Page : 184 pages
File Size : 53,8 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.

Metaheuristics for Big Data

Clarisse Dhaenens,Laetitia Jourdan
Metaheuristics for Big Data Book PDF

Author : Clarisse Dhaenens,Laetitia Jourdan
Publisher : John Wiley & Sons
Page : 228 pages
File Size : 51,5 Mb
Release : 2016-08-29
Category : Computers
ISBN : 9781848218062

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Metaheuristics for Big Data by Clarisse Dhaenens,Laetitia Jourdan Pdf

Big Data is a new field, with many technological challenges to be understood in order to use it to its full potential. These challenges arise at all stages of working with Big Data, beginning with data generation and acquisition. The storage and management phase presents two critical challenges: infrastructure, for storage and transportation, and conceptual models. Finally, to extract meaning from Big Data requires complex analysis. Here the authors propose using metaheuristics as a solution to these challenges; they are first able to deal with large size problems and secondly flexible and therefore easily adaptable to different types of data and different contexts. The use of metaheuristics to overcome some of these data mining challenges is introduced and justified in the first part of the book, alongside a specific protocol for the performance evaluation of algorithms. An introduction to metaheuristics follows. The second part of the book details a number of data mining tasks, including clustering, association rules, supervised classification and feature selection, before explaining how metaheuristics can be used to deal with them. This book is designed to be self-contained, so that readers can understand all of the concepts discussed within it, and to provide an overview of recent applications of metaheuristics to knowledge discovery problems in the context of Big Data.

Intelligent Data Engineering and Automated Learning – IDEAL 2020

Cesar Analide,Paulo Novais,David Camacho,Hujun Yin
Intelligent Data Engineering and Automated Learning IDEAL 2020 Book PDF

Author : Cesar Analide,Paulo Novais,David Camacho,Hujun Yin
Publisher : Springer Nature
Page : 424 pages
File Size : 42,6 Mb
Release : 2020-10-29
Category : Computers
ISBN : 9783030623623

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 by Cesar Analide,Paulo Novais,David Camacho,Hujun Yin Pdf

This two-volume set of LNCS 12489 and 12490 constitutes the thoroughly refereed conference proceedings of the 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020, held in Guimaraes, Portugal, in November 2020.* The 93 papers presented were carefully reviewed and selected from 134 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2020 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspiredmodels, agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI. * The conference was held virtually due to the COVID-19 pandemic.

Bioinformatics and Human Genomics Research

Diego A. Forero
Bioinformatics and Human Genomics Research Book PDF

Author : Diego A. Forero
Publisher : CRC Press
Page : 374 pages
File Size : 47,7 Mb
Release : 2021-12-22
Category : Science
ISBN : 9781000405675

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Bioinformatics and Human Genomics Research by Diego A. Forero Pdf

Advances in high-throughput biological methods have led to the publication of a large number of genome-wide studies in human and animal models. In this context, recent tools from bioinformatics and computational biology have been fundamental for the analysis of these genomic studies. The book Bioinformatics and Human Genomics Research provides updated and comprehensive information about multiple approaches of the application of bioinformatic tools to research in human genomics. It covers strategies analysis of genome-wide association studies, genome-wide expression studies and genome-wide DNA methylation, among other topics. It provides interesting strategies for data mining in human genomics, network analysis, prediction of binding sites for miRNAs and transcription factors, among other themes. Experts from all around the world in bioinformatics and human genomics have contributed chapters in this book. Readers will find this book as quite useful for their in silico explorations, which would contribute to a better and deeper understanding of multiple biological processes and of pathophysiology of many human diseases.