Download or read book A Comparison of Genetic Algorithms and Other Machine Learning Systems on a Complex Classification Task from Common Disease Research written by Clare Bates Congdon and published by . This book was released on 1995 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "The thesis project is an investigation of some well- known machine learning systems and evaluates their utility when applied to a classification task from the field of human genetics. This common- disease research task, an inquiry into genetic and biochemical factors and their association with a family history of coronary artery disease (CAD), is more complex than many pursued in machine learning research, due to interactions and the inherent noise in the dataset. The task also differs from most pursued in machine learning research because there is a desire to explain the dataset with a small number of rules, even at the expense of accuracy, so that they will be more accessible to medical researchers who are unaccustomed to dealing with disjunctive explanations of data. Furthermore, there is assymetry in the task in that good explanations of the positive examples is of more importance than good explanations of the negative examples. The primary machine learning approach investigated in this research is genetic algorithms (GA's); decision trees, Autoclass, and Cobweb are also included. The GA performed the best in terms of descriptive ability with the common-disease research task, although decision trees also demonstrated certain strengths. Autoclass and Cobweb were recognized from the onset as being inappropriate for the needs of common-disease researchers (because both systems are unsupervised learners that create probabilistic structures), but were included for their interest in the machine learning community; these systems did not perform as well as GA's and decision trees in terms of their ability to describe the data. In terms of predictive accuracy, all systems performed poorly, and the differences between any two of the three best systems is not significant. When positive and negative examples are considered separately, the GA does significantly better than the other systems in predicting positive examples and significantly worse in predicting negative examples. The thesis illustrates that the investigation of 'real' problems from researchers in other fields can lead machine learning researchers to challenge their systems in ways they may not otherwise have considered, and may lead these researchers to a symbiotic relationship that benefits multiple research communities."

Download or read book Evolutionary Computation in Bioinformatics written by Gary Fogel and published by Morgan Kaufmann. This book was released on 2003 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a definitive resource that bridges biology and evolutionary computation. The authors have written an introduction to biology and bioinformatics for computer scientists, plus an introduction to evolutionary computation for biologists and for computer scientists unfamiliar with these techniques.

Download or read book Hybrid Artificial Intelligent Systems written by Emilio S. Corchado Rodriguez and published by Springer. This book was released on 2012-03-15 with total page 739 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two LNAI volumes 7208 and 7209 constitute the proceedings of the 7th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2012, held in Salamanca, Spain, in March 2012. The 118 papers published in these proceedings were carefully reviewed and selected from 293 submissions. They are organized in topical sessions on agents and multi agents systems, HAIS applications, cluster analysis, data mining and knowledge discovery, evolutionary computation, learning algorithms, systems, man, and cybernetics by HAIS workshop, methods of classifier fusion, HAIS for computer security (HAISFCS), data mining: data preparation and analysis, hybrid artificial intelligence systems in management of production systems, hybrid artificial intelligent systems for ordinal regression, hybrid metaheuristics for combinatorial optimization and modelling complex systems, hybrid computational intelligence and lattice computing for image and signal processing and nonstationary models of pattern recognition and classifier combinations.

Download or read book Methods in Biomedical Informatics written by Indra Neil Sarkar and published by Academic Press. This book was released on 2013-09-03 with total page 589 pages. Available in PDF, EPUB and Kindle. Book excerpt: Beginning with a survey of fundamental concepts associated with data integration, knowledge representation, and hypothesis generation from heterogeneous data sets, Methods in Biomedical Informatics provides a practical survey of methodologies used in biological, clinical, and public health contexts. These concepts provide the foundation for more advanced topics like information retrieval, natural language processing, Bayesian modeling, and learning classifier systems. The survey of topics then concludes with an exposition of essential methods associated with engineering, personalized medicine, and linking of genomic and clinical data. Within an overall context of the scientific method, Methods in Biomedical Informatics provides a practical coverage of topics that is specifically designed for: (1) domain experts seeking an understanding of biomedical informatics approaches for addressing specific methodological needs; or (2) biomedical informaticians seeking an approachable overview of methodologies that can be used in scenarios germane to biomedical research. - Contributors represent leading biomedical informatics experts: individuals who have demonstrated effective use of biomedical informatics methodologies in the real-world, high-quality biomedical applications - Material is presented as a balance between foundational coverage of core topics in biomedical informatics with practical "in-the-trenches" scenarios. - Contains appendices that function as primers on: (1) Unix; (2) Ruby; (3) Databases; and (4) Web Services.

Download or read book Biological and Medical Data Analysis written by José María Barreiro and published by Springer Science & Business Media. This book was released on 2004-11-18 with total page 519 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Symposium on Biological and Medical Data Analysis, ISBMDA 2004, held in Barcelona, Spain in November 2004. The 50 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on data analysis for image processing, data visualization, decision support systems, information retrieval, knowledge discovery and data mining, statistical methods and tools, time series analysis, data management and analysis in bioinformatics, integration of biological and medical data, metabolic data and pathways, and microarray data analysis and visualization.

Download or read book Introduction to Genetic Algorithms written by S.N. Sivanandam and published by Springer Science & Business Media. This book was released on 2007-10-24 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a basic introduction to genetic algorithms. It provides a detailed explanation of genetic algorithm concepts and examines numerous genetic algorithm optimization problems. In addition, the book presents implementation of optimization problems using C and C++ as well as simulated solutions for genetic algorithm problems using MATLAB 7.0. It also includes application case studies on genetic algorithms in emerging fields.

Download or read book Proceedings of the International IEEE Conference on Tools for Artificial Intelligence written by and published by . This book was released on 2000 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Classification and Learning Using Genetic Algorithms written by Sanghamitra Bandyopadhyay and published by Springer Science & Business Media. This book was released on 2007-05-17 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks.

Download or read book Efficient Decision Support Systems written by Chiang Jao and published by BoD – Books on Demand. This book was released on 2011-09-06 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers.

Download or read book Dissertation Abstracts International written by and published by . This book was released on 2006 with total page 774 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Genetic Algorithms for Machine Learning written by John J. Grefenstette and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: The articles presented here were selected from preliminary versions presented at the International Conference on Genetic Algorithms in June 1991, as well as at a special Workshop on Genetic Algorithms for Machine Learning at the same Conference. Genetic algorithms are general-purpose search algorithms that use principles inspired by natural population genetics to evolve solutions to problems. The basic idea is to maintain a population of knowledge structure that represent candidate solutions to the problem of interest. The population evolves over time through a process of competition (i.e. survival of the fittest) and controlled variation (i.e. recombination and mutation). Genetic Algorithms for Machine Learning contains articles on three topics that have not been the focus of many previous articles on GAs, namely concept learning from examples, reinforcement learning for control, and theoretical analysis of GAs. It is hoped that this sample will serve to broaden the acquaintance of the general machine learning community with the major areas of work on GAs. The articles in this book address a number of central issues in applying GAs to machine learning problems. For example, the choice of appropriate representation and the corresponding set of genetic learning operators is an important set of decisions facing a user of a genetic algorithm. The study of genetic algorithms is proceeding at a robust pace. If experimental progress and theoretical understanding continue to evolve as expected, genetic algorithms will continue to provide a distinctive approach to machine learning. Genetic Algorithms for Machine Learning is an edited volume of original research made up of invited contributions by leading researchers.

Download or read book Biological and Medical Data Analysis written by and published by . This book was released on 2004 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Genetic Programming Approach to Classification Problems written by Hakan Uysal and published by GRIN Verlag. This book was released on 2016-07-26 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essay from the year 2013 in the subject Computer Science - Programming, grade: A+, University College Dublin, course: Natural Computing, language: English, abstract: Genetic Programming is a biological evolution inspired technique for computer programs to solve problems automatically by evolving iteratively using a fitness function. The advantage of this type programming is that it only defines the basics. As a result of this, it is a flexible solution for broad range of domains. Classification has been one of the most compelling problems in machine learning. In this paper, there is a comparison between genetic programming classifier and conventional classification algorithms like Naive Bayes, C4.5 decision tree, Random Forest, Support Vector Machines and k-Nearest Neighbour. The experiment is done on several data sets with different sizes, feature sets and attribute properties. There is also an experiment on the time complexity of each classifier method.

Download or read book Festschrift in Honor of John H Holland written by and published by . This book was released on 1999 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Analysis of Biological Data written by Sanghamitra Bandyopadhyay and published by World Scientific. This book was released on 2007 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bioinformatics, a field devoted to the interpretation and analysis of biological data using computational techniques, has evolved tremendously in recent years due to the explosive growth of biological information generated by the scientific community. Soft computing is a consortium of methodologies that work synergistically and provides, in one form or another, flexible information processing capabilities for handling real-life ambiguous situations. Several research articles dealing with the application of soft computing tools to bioinformatics have been published in the recent past; however, they are scattered in different journals, conference proceedings and technical reports, thus causing inconvenience to readers, students and researchers. This book, unique in its nature, is aimed at providing a treatise in a unified framework, with both theoretical and experimental results, describing the basic principles of soft computing and demonstrating the various ways in which they can be used for analyzing biological data in an efficient manner. Interesting research articles from eminent scientists around the world are brought together in a systematic way such that the reader will be able to understand the issues and challenges in this domain, the existing ways of tackling them, recent trends, and future directions. This book is the first of its kind to bring together two important research areas, soft computing and bioinformatics, in order to demonstrate how the tools and techniques in the former can be used for efficiently solving several problems in the latter. Sample Chapter(s). Chapter 1: Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments (160 KB). Contents: Overview: Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments (H Tang & S Kim); An Introduction to Soft Computing (A Konar & S Das); Biological Sequence and Structure Analysis: Reconstructing Phylogenies with Memetic Algorithms and Branch-and-Bound (J E Gallardo et al.); Classification of RNA Sequences with Support Vector Machines (J T L Wang & X Wu); Beyond String Algorithms: Protein Sequence Analysis Using Wavelet Transforms (A Krishnan & K-B Li); Filtering Protein Surface Motifs Using Negative Instances of Active Sites Candidates (N L Shrestha & T Ohkawa); Distill: A Machine Learning Approach to Ab Initio Protein Structure Prediction (G Pollastri et al.); In Silico Design of Ligands Using Properties of Target Active Sites (S Bandyopadhyay et al.); Gene Expression and Microarray Data Analysis: Inferring Regulations in a Genomic Network from Gene Expression Profiles (N Noman & H Iba); A Reliable Classification of Gene Clusters for Cancer Samples Using a Hybrid Multi-Objective Evolutionary Procedure (K Deb et al.); Feature Selection for Cancer Classification Using Ant Colony Optimization and Support Vector Machines (A Gupta et al.); Sophisticated Methods for Cancer Classification Using Microarray Data (S-B Cho & H-S Park); Multiobjective Evolutionary Approach to Fuzzy Clustering of Microarray Data (A Mukhopadhyay et al.). Readership: Graduate students and researchers in computer science, bioinformatics, computational and molecular biology, artificial intelligence, data mining, machine learning, electrical engineering, system science; researchers in pharmaceutical industries.

Download or read book The Practical Handbook of Genetic Algorithms written by Lance D. Chambers and published by CRC Press. This book was released on 2019-09-17 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: The mathematics employed by genetic algorithms (GAs)are among the most exciting discoveries of the last few decades. But what exactly is a genetic algorithm? A genetic algorithm is a problem-solving method that uses genetics as its model of problem solving. It applies the rules of reproduction, gene crossover, and mutation to pseudo-organism

Download or read book Genetic Algorithms for Pattern Recognition written by Sankar K. Pal and published by CRC Press. This book was released on 2017-11-22 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science and technology, describing the integration of genetic algorithms in pattern recognition and machine learning problems to build intelligent recognition systems. The articles, written by leading experts from around the world, accomplish several objectives: they provide insight into the theory of genetic algorithms; they develop pattern recognition theory in light of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research presented in Genetic Algorithms for Pattern Recognition makes it a unique text, ideal for graduate students and researchers.