Meta Learning In Decision Tree Induction

Author : Krzysztof Grąbczewski
Publisher : Springer
Page : 349 pages
File Size : 49,6 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 : Krzysztof Gr Bczewski
Publisher : Unknown
Page : 360 pages
File Size : 52,5 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 : Rodrigo C. Barros,André C.P.L.F de Carvalho,Alex A. Freitas
Publisher : Springer
Page : 184 pages
File Size : 42,9 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 : Ilias Maglogiannis,Lazaros Iliadis,John Macintyre,Paulo Cortez
Publisher : Springer Nature
Page : 528 pages
File Size : 46,7 Mb
Release : 2022-06-16
Category : Computers
ISBN : 9783031083372

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Artificial Intelligence Applications and Innovations by Ilias Maglogiannis,Lazaros Iliadis,John Macintyre,Paulo Cortez Pdf

This book constitutes the refereed proceedings of five International Workshops held as parallel events of the 18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022, virtually and in Hersonissos, Crete, Greece, in June 2022: the 11th Mining Humanistic Data Workshop (MHDW 2022); the 7th 5G-Putting Intelligence to the Network Edge Workshop (5G-PINE 2022); the 1st workshop on AI in Energy, Building and Micro-Grids (AIBMG 2022); the 1st Workshop/Special Session on Machine Learning and Big Data in Health Care (ML@HC 2022); and the 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics (AIBEI 2022). The 35 full papers presented at these workshops were carefully reviewed and selected from 74 submissions.

Author : Steffen Lange,Ken Satoh,Carl H. Smith
Publisher : Springer
Page : 470 pages
File Size : 43,6 Mb
Release : 2003-08-03
Category : Computers
ISBN : 9783540361824

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Discovery Science by Steffen Lange,Ken Satoh,Carl H. Smith Pdf

This volume contains the papers presented at the 5th International Conference on Discovery Science (DS 2002) held at the Mövenpick Hotel, Lub ̈eck, G- many, November 24-26, 2002. The conference was supported by CorpoBase, DFKI GmbH, and JessenLenz. The conference was collocated with the 13th International Conference on - gorithmic Learning Theory (ALT 2002). Both conferences were held in parallel and shared?ve invited talks as well as all social events. The combination of ALT 2002 and DS 2002 allowed for a comprehensive treatment of recent de- lopments in computational learning theory and machine learning - some of the cornerstones of discovery science. In response to the call for papers 76 submissions were received. The program committee selected 17 submissions as regular papers and 29 submissions as poster presentations of which 27 have been submitted for publication. This selection was based on clarity, signi?cance, and originality, as well as on relevance to the rapidly evolving?eld of discovery science.

Author : Lior Rokach,Oded Z. Maimon
Publisher : World Scientific
Page : 263 pages
File Size : 53,7 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 : Sašo Džeroski,Bart Goethals,Panče Panov
Publisher : Springer Science & Business Media
Page : 458 pages
File Size : 49,5 Mb
Release : 2010-11-18
Category : Computers
ISBN : 9781441977380

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Inductive Databases and Constraint-Based Data Mining by Sašo Džeroski,Bart Goethals,Panče Panov Pdf

This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and - citing research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the uni?cation of pattern mining approaches through constraint programming, the clari?cation of the re- tionship between mining local patterns and global models, and the proposed in- grative frameworks and approaches for inducive databases. On the application side, applications to practically relevant problems from bioinformatics are presented. Inductive databases (IDBs) represent a database view on data mining and kno- edge discovery. IDBs contain not only data, but also generalizations (patterns and models) valid in the data. In an IDB, ordinary queries can be used to access and - nipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns and models. In the IDB framework, patterns and models become ”?rst-class citizens” and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried.

Author : Pijush Dutta,Souvik Pal,Asok Kumar,Korhan Cengiz
Publisher : CRC Press
Page : 295 pages
File Size : 47,6 Mb
Release : 2023-04-19
Category : Computers
ISBN : 9781000864199

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Artificial Intelligence for Cognitive Modeling by Pijush Dutta,Souvik Pal,Asok Kumar,Korhan Cengiz Pdf

This book is written in a clear and thorough way to cover both the traditional and modern uses of artificial intelligence and soft computing. It gives an in-depth look at mathematical models, algorithms, and real-world problems that are hard to solve in MATLAB. The book is intended to provide a broad and in-depth understanding of fuzzy logic controllers, genetic algorithms, neural networks, and hybrid techniques such as ANFIS and the GA-ANN model. Features: A detailed description of basic intelligent techniques (fuzzy logic, genetic algorithm and neural network using MATLAB) A detailed description of the hybrid intelligent technique called the adaptive fuzzy inference technique (ANFIS) Formulation of the nonlinear model like analysis of ANOVA and response surface methodology Variety of solved problems on ANOVA and RSM Case studies of above mentioned intelligent techniques on the different process control systems This book can be used as a handbook and a guide for students of all engineering disciplines, operational research areas, computer applications, and for various professionals who work in the optimization area.

Author : Hillol Kargupta
Publisher : SIAM
Page : 670 pages
File Size : 55,9 Mb
Release : 2005-04-01
Category : Mathematics
ISBN : 0898715938

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Proceedings of the Fifth SIAM International Conference on Data Mining by Hillol Kargupta Pdf

The Fifth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. Advances in information technology and data collection methods have led to the availability of large data sets in commercial enterprises and in a wide variety of scientific and engineering disciplines. The field of data mining draws upon extensive work in areas such as statistics, machine learning, pattern recognition, databases, and high performance computing to discover interesting and previously unknown information in data. This conference results in data mining, including applications, algorithms, software, and systems.

Author : Robert Burduk,Marek Kurzynski,Michal Wozniak,Andrzej Zolnierek
Publisher : Springer Science & Business Media
Page : 761 pages
File Size : 41,5 Mb
Release : 2011-04-21
Category : Technology & Engineering
ISBN : 9783642203206

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Computer Recognition Systems 4 by Robert Burduk,Marek Kurzynski,Michal Wozniak,Andrzej Zolnierek Pdf

The computer recognition systems are nowadays one of the most promising directions in artificial intelligence. This book is the most comprehensive study of this field. It contains a collection of 78 carefully selected articles contributed by experts of pattern recognition. It reports on current research with respect to both methodology and applications. In particular, it includes the following sections: Biometrics, Features, learning and classifiers, Image processing and computer vision, Knowledge acquisition based on reasoning methods Medical applications, Miscellaneous applications, This book is a great reference tool for scientists who deal with the problems of designing computer pattern recognition systems. Its target readers can be as well researchers as students of computer science, artificial intelligence or robotics.

Author : Oded Maimon,Lior Rokach
Publisher : Springer Science & Business Media
Page : 1269 pages
File Size : 47,7 Mb
Release : 2010-09-10
Category : Computers
ISBN : 9780387098234

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Data Mining and Knowledge Discovery Handbook by Oded Maimon,Lior Rokach Pdf

This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.

Author : Oded Z. Maimon,Oded Maimon,Lior Rokach
Publisher : Springer Science & Business Media
Page : 1436 pages
File Size : 51,9 Mb
Release : 2005
Category : Computers
ISBN : 0387244352

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Data Mining and Knowledge Discovery Handbook by Oded Z. Maimon,Oded Maimon,Lior Rokach Pdf

Organizes major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD). This book provides algorithmic descriptions of classic methods, and also suitable for professionals in fields such as computing applications, information systems management, and more.

Author : Alberto Fernández,Salvador García,Mikel Galar,Ronaldo C. Prati,Bartosz Krawczyk,Francisco Herrera
Publisher : Springer
Page : 377 pages
File Size : 47,8 Mb
Release : 2018-10-22
Category : Computers
ISBN : 9783319980744

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Learning from Imbalanced Data Sets by Alberto Fernández,Salvador García,Mikel Galar,Ronaldo C. Prati,Bartosz Krawczyk,Francisco Herrera Pdf

This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.

Author : Gisele L. Pappa,Alex Freitas
Publisher : Springer Science & Business Media
Page : 198 pages
File Size : 50,9 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 : Marek Kretowski
Publisher : Springer
Page : 180 pages
File Size : 40,6 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.