Decision Tree And Ensemble Learning Based On Ant Colony Optimization

Author : Jan Kozak
Publisher : Springer
Page : 159 pages
File Size : 47,9 Mb
Release : 2018-06-20
Category : Technology & Engineering
ISBN : 9783319937526

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Decision Tree and Ensemble Learning Based on Ant Colony Optimization by Jan Kozak Pdf

This book not only discusses the important topics in the area of machine learning and combinatorial optimization, it also combines them into one. This was decisive for choosing the material to be included in the book and determining its order of presentation. Decision trees are a popular method of classification as well as of knowledge representation. At the same time, they are easy to implement as the building blocks of an ensemble of classifiers. Admittedly, however, the task of constructing a near-optimal decision tree is a very complex process. The good results typically achieved by the ant colony optimization algorithms when dealing with combinatorial optimization problems suggest the possibility of also using that approach for effectively constructing decision trees. The underlying rationale is that both problem classes can be presented as graphs. This fact leads to option of considering a larger spectrum of solutions than those based on the heuristic. Moreover, ant colony optimization algorithms can be used to advantage when building ensembles of classifiers. This book is a combination of a research monograph and a textbook. It can be used in graduate courses, but is also of interest to researchers, both specialists in machine learning and those applying machine learning methods to cope with problems from any field of R&D.

Author : Jan Kozak
Publisher : Unknown
Page : 159 pages
File Size : 52,9 Mb
Release : 2019
Category : Ant algorithms
ISBN : 3319937537

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Decision Tree and Ensemble Learning Based on Ant Colony Optimization by Jan Kozak Pdf

This book not only discusses the important topics in the area of machine learning and combinatorial optimization, it also combines them into one. This was decisive for choosing the material to be included in the book and determining its order of presentation. Decision trees are a popular method of classification as well as of knowledge representation. At the same time, they are easy to implement as the building blocks of an ensemble of classifiers. Admittedly, however, the task of constructing a near-optimal decision tree is a very complex process. The good results typically achieved by the ant colony optimization algorithms when dealing with combinatorial optimization problems suggest the possibility of also using that approach for effectively constructing decision trees. The underlying rationale is that both problem classes can be presented as graphs. This fact leads to option of considering a larger spectrum of solutions than those based on the heuristic. Moreover, ant colony optimization algorithms can be used to advantage when building ensembles of classifiers. This book is a combination of a research monograph and a textbook. It can be used in graduate courses, but is also of interest to researchers, both specialists in machine learning and those applying machine learning methods to cope with problems from any field of R & D.

Author : Satchidananda Dehuri,Bhabani Shankar Prasad Mishra,Pradeep Kumar Mallick,Sung-Bae Cho,Margarita N. Favorskaya
Publisher : Springer Nature
Page : 268 pages
File Size : 51,6 Mb
Release : 2020-01-21
Category : Technology & Engineering
ISBN : 9783030390334

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Biologically Inspired Techniques in Many-Criteria Decision Making by Satchidananda Dehuri,Bhabani Shankar Prasad Mishra,Pradeep Kumar Mallick,Sung-Bae Cho,Margarita N. Favorskaya Pdf

This book addresses many-criteria decision-making (MCDM), a process used to find a solution in an environment with several criteria. In many real-world problems, there are several different objectives that need to be taken into account. Solving these problems is a challenging task and requires careful consideration. In real applications, often simple and easy to understand methods are used; as a result, the solutions accepted by decision makers are not always optimal solutions. On the other hand, algorithms that would provide better outcomes are very time consuming. The greatest challenge facing researchers is how to create effective algorithms that will yield optimal solutions with low time complexity. Accordingly, many current research efforts are focused on the implementation of biologically inspired algorithms (BIAs), which are well suited to solving uni-objective problems. This book introduces readers to state-of-the-art developments in biologically inspired techniques and their applications, with a major emphasis on the MCDM process. To do so, it presents a wide range of contributions on e.g. BIAs, MCDM, nature-inspired algorithms, multi-criteria optimization, machine learning and soft computing.

Author : Jayaraman Valadi
Publisher : Springer Nature
Page : 365 pages
File Size : 52,7 Mb
Release : 2024-05-17
Category : Electronic
ISBN : 9789819997183

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Advanced Machine Learning with Evolutionary and Metaheuristic Techniques by Jayaraman Valadi Pdf

Author : Marek Kretowski
Publisher : Springer
Page : 180 pages
File Size : 44,9 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.

Author : Lior Rokach,Oded Z. Maimon
Publisher : World Scientific
Page : 263 pages
File Size : 50,5 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 : Oluwatobi Adeleke,Sina Karimzadeh,Tien-Chien Jen
Publisher : CRC Press
Page : 353 pages
File Size : 42,5 Mb
Release : 2023-12-15
Category : Technology & Engineering
ISBN : 9781003803331

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Machine Learning-Based Modelling in Atomic Layer Deposition Processes by Oluwatobi Adeleke,Sina Karimzadeh,Tien-Chien Jen Pdf

While thin film technology has benefited greatly from artificial intelligence (AI) and machine learning (ML) techniques, there is still much to be learned from a full-scale exploration of these technologies in atomic layer deposition (ALD). This book provides in-depth information regarding the application of ML-based modeling techniques in thin film technology as a standalone approach and integrated with the classical simulation and modeling methods. It is the first of its kind to present detailed information regarding approaches in ML-based modeling, optimization, and prediction of the behaviors and characteristics of ALD for improved process quality control and discovery of new materials. As such, this book fills significant knowledge gaps in the existing resources as it provides extensive information on ML and its applications in film thin technology. Offers an in-depth overview of the fundamentals of thin film technology, state-of-the-art computational simulation approaches in ALD, ML techniques, algorithms, applications, and challenges. Establishes the need for and significance of ML applications in ALD while introducing integration approaches for ML techniques with computation simulation approaches. Explores the application of key techniques in ML, such as predictive analysis, classification techniques, feature engineering, image processing capability, and microstructural analysis of deep learning algorithms and generative model benefits in ALD. Helps readers gain a holistic understanding of the exciting applications of ML-based solutions to ALD problems and apply them to real-world issues. Aimed at materials scientists and engineers, this book fills significant knowledge gaps in existing resources as it provides extensive information on ML and its applications in film thin technology. It also opens space for future intensive research and intriguing opportunities for ML-enhanced ALD processes, which scale from academic to industrial applications. . .

Author : Ngoc Thanh Nguyen,Lazaros Iliadis,Ilias Maglogiannis,Bogdan Trawiński
Publisher : Springer Nature
Page : 817 pages
File Size : 49,6 Mb
Release : 2021-09-29
Category : Computers
ISBN : 9783030880811

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Computational Collective Intelligence by Ngoc Thanh Nguyen,Lazaros Iliadis,Ilias Maglogiannis,Bogdan Trawiński Pdf

This book constitutes the refereed proceedings of the 13th International Conference on Computational Collective Intelligence, ICCCI 2021, held in September/October 2021. The conference was held virtually due to the COVID-19 pandemic. The 58 full papers were carefully reviewed and selected from 230 submissions. The papers are grouped in topical issues on knowledge engineering and semantic web; social networks and recommender systems; collective decision-making; cooperative strategies for decision making and optimization; data mining and machine learning; computer vision techniques; natural language processing; Internet of Things: technologies and applications; Internet of Things and computational technologies for collective intelligence; computational intelligence for multimedia understanding.

Author : Marco Dorigo,Thomas Stutzle
Publisher : MIT Press
Page : 324 pages
File Size : 44,5 Mb
Release : 2004-06-04
Category : Computers
ISBN : 0262042193

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Ant Colony Optimization by Marco Dorigo,Thomas Stutzle Pdf

An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.

Author : Adel Ali Abou El-Ela,Mohamed T. Mouwafi,Adel A. Elbaset
Publisher : Springer Nature
Page : 237 pages
File Size : 42,8 Mb
Release : 2022-09-15
Category : Technology & Engineering
ISBN : 9783030960254

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Modern Optimization Techniques for Smart Grids by Adel Ali Abou El-Ela,Mohamed T. Mouwafi,Adel A. Elbaset Pdf

Modern Optimization Techniques for Smart Grids presents current research and methods for monitoring transmission systems and enhancing distribution system performance using optimization techniques considering the role of different single and multi-objective functions. The authors present in-depth information on integrated systems for smart transmission and distribution, including using smart meters such as phasor measurement units (PMUs), enhancing distribution system performance using the optimal placement of distributed generations (DGs) and/or capacitor banks, and optimal capacitor placement for power loss reduction and voltage profile improvement. The book will be a valuable reference for researchers, students, and engineers working in electrical power engineering and renewable energy systems. Predicts future development of hybrid power systems; Introduces enhanced optimization strategies; Includes MATLAB M-file codes.

Author : Yassine Maleh,Mamoun Alazab,Noreddine Gherabi,Lo’ai Tawalbeh,Ahmed A. Abd El-Latif
Publisher : Springer Nature
Page : 621 pages
File Size : 47,9 Mb
Release : 2022-01-12
Category : Technology & Engineering
ISBN : 9783030917388

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Advances in Information, Communication and Cybersecurity by Yassine Maleh,Mamoun Alazab,Noreddine Gherabi,Lo’ai Tawalbeh,Ahmed A. Abd El-Latif Pdf

This book gathers the proceedings of the International Conference on Information, Communication and Cybersecurity, held on November 10–11, 2021, in Khouribga, Morocco. The conference was jointly coorganized by The National School of Applied Sciences of Sultan Moulay Slimane University, Morocco, and Charles Darwin University, Australia. This book provides an opportunity to account for state-of-the-art works, future trends impacting information technology, communications, and cybersecurity, focusing on elucidating the challenges, opportunities, and inter-dependencies that are just around the corner. This book is helpful for students and researchers as well as practitioners. ICI2C 2021 was devoted to advances in smart information technologies, communication, and cybersecurity. It was considered a meeting point for researchers and practitioners to implement advanced information technologies into various industries. There were 159 paper submissions from 24 countries. Each submission was reviewed by at least three chairs or PC members. We accepted 54 regular papers (34\%). Unfortunately, due to limitations of conference topics and edited volumes, the Program Committee was forced to reject some interesting papers, which did not satisfy these topics or publisher requirements. We would like to thank all authors and reviewers for their work and valuable contributions. The friendly and welcoming attitude of conference supporters and contributors made this event a success!

Author : Tshilidzi Marwala,Collins Achepsah Leke
Publisher : World Scientific
Page : 321 pages
File Size : 43,7 Mb
Release : 2019-11-21
Category : Computers
ISBN : 9789811205682

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Handbook Of Machine Learning - Volume 2: Optimization And Decision Making by Tshilidzi Marwala,Collins Achepsah Leke Pdf

Building on , this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts.

Author : Raj, Alex Noel Joseph,Mahesh, Vijayalakshmi G. V.,Nerssison, Ruban,Yu, Ang,Gentry, Jennifer
Publisher : IGI Global
Page : 293 pages
File Size : 50,7 Mb
Release : 2022-06-24
Category : Law
ISBN : 9781668445600

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Aiding Forensic Investigation Through Deep Learning and Machine Learning Frameworks by Raj, Alex Noel Joseph,Mahesh, Vijayalakshmi G. V.,Nerssison, Ruban,Yu, Ang,Gentry, Jennifer Pdf

It is crucial that forensic science meets challenges such as identifying hidden patterns in data, validating results for accuracy, and understanding varying criminal activities in order to be authoritative so as to hold up justice and public safety. Artificial intelligence, with its potential subsets of machine learning and deep learning, has the potential to transform the domain of forensic science by handling diverse data, recognizing patterns, and analyzing, interpreting, and presenting results. Machine Learning and deep learning frameworks, with developed mathematical and computational tools, facilitate the investigators to provide reliable results. Further study on the potential uses of these technologies is required to better understand their benefits. Aiding Forensic Investigation Through Deep Learning and Machine Learning Frameworks provides an outline of deep learning and machine learning frameworks and methods for use in forensic science to produce accurate and reliable results to aid investigation processes. The book also considers the challenges, developments, advancements, and emerging approaches of deep learning and machine learning. Covering key topics such as biometrics, augmented reality, and fraud investigation, this reference work is crucial for forensic scientists, law enforcement, computer scientists, researchers, scholars, academicians, practitioners, instructors, and students.

Author : Joseph B. Rivera
Publisher : Joseph B. Rivera
Page : 189 pages
File Size : 46,9 Mb
Release : 2020-02-17
Category : Business & Economics
ISBN : 8210379456XXX

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Marketing Analytics: Creating Customer Centric Culture by Joseph B. Rivera Pdf

A game-changing approach to marketing by an experienced author, speaker and businessman Joseph B. Rivera. Joseph B. Rivera has first-hand experience in business. He has learned everything through hard work and perseverance, and has inspired quite a lot of entrepreneurs, businessmen, executives, employees, and business students to challenge themselves in this modern era of commerce. For the first time, Joseph B. Rivera offers his years of experience and wisdom in this one compact, very accessible and enduring masterpiece. MARKETING ANALYTICS: CREATING CUSTOMER-CENTRIC CULTURE helps you to create a transformative culture toward excellence in your business. Whether you are an executive, businessman, business owner, investor, marketer, trainer, speaker or a student of marketing, you will be proud of what you will learn. When applied right, you will change the way products and services are designed, created and offered to the world. This book teaches you how to meaningfully connect emotionally and practically to your consumers. Remember, it is not just all about the money. Here, Joseph has put together his passion, insights, observation and experience to mentor you: ✔️How to understand the needs of the market. ✔️How to position your business. ✔️How to overcome competition. ✔️How to revolutionize your business. Learn the art or marketing analytics, and be a game changer.

Author : George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain
Publisher : Springer Nature
Page : 237 pages
File Size : 51,8 Mb
Release : 2021-08-05
Category : Technology & Engineering
ISBN : 9783030767945

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Advances in Machine Learning/Deep Learning-based Technologies by George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain Pdf

As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction. This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.