Download or read book Decision Trees with Hypotheses written by Mohammad Azad and published by Springer Nature. This book was released on 2022-11-18 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, the concept of a hypothesis about the values of all attributes is added to the standard decision tree model, considered, in particular, in test theory and rough set theory. This extension allows us to use the analog of equivalence queries from exact learning and explore decision trees that are based on various combinations of attributes, hypotheses, and proper hypotheses (analog of proper equivalence queries). The two main goals of this book are (i) to provide tools for the experimental and theoretical study of decision trees with hypotheses and (ii) to compare these decision trees with conventional decision trees that use only queries, each based on a single attribute. Both experimental and theoretical results show that decision trees with hypotheses can have less complexity than conventional decision trees. These results open up some prospects for using decision trees with hypotheses as a means of knowledge representation and algorithms for computing Boolean functions. The obtained theoretical results and tools for studying decision trees with hypotheses are useful for researchers using decision trees and rules in data analysis. This book can also be used as the basis for graduate courses.
Download or read book Data Mining with Decision Trees written by Lior Rokach and published by World Scientific. This book was released on 2008 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Average Time Complexity of Decision Trees written by Igor Chikalov and published by Springer Science & Business Media. This book was released on 2011-08-04 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision tree is a widely used form of representing algorithms and knowledge. Compact data models and fast algorithms require optimization of tree complexity. This book is a research monograph on average time complexity of decision trees. It generalizes several known results and considers a number of new problems. The book contains exact and approximate algorithms for decision tree optimization, and bounds on minimum average time complexity of decision trees. Methods of combinatorics, probability theory and complexity theory are used in the proofs as well as concepts from various branches of discrete mathematics and computer science. The considered applications include the study of average depth of decision trees for Boolean functions from closed classes, the comparison of results of the performance of greedy heuristics for average depth minimization with optimal decision trees constructed by dynamic programming algorithm, and optimization of decision trees for the corner point recognition problem from computer vision. The book can be interesting for researchers working on time complexity of algorithms and specialists in test theory, rough set theory, logical analysis of data and machine learning.
Download or read book Comparative Analysis of Deterministic and Nondeterministic Decision Trees written by Mikhail Moshkov and published by Springer Nature. This book was released on 2020-03-14 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book compares four parameters of problems in arbitrary information systems: complexity of problem representation and complexity of deterministic, nondeterministic, and strongly nondeterministic decision trees for problem solving. Deterministic decision trees are widely used as classifiers, as a means of knowledge representation, and as algorithms. Nondeterministic (strongly nondeterministic) decision trees can be interpreted as systems of true decision rules that cover all objects (objects from one decision class). This book develops tools for the study of decision trees, including bounds on complexity and algorithms for construction of decision trees for decision tables with many-valued decisions. It considers two approaches to the investigation of decision trees for problems in information systems: local, when decision trees can use only attributes from the problem representation; and global, when decision trees can use arbitrary attributes from the information system. For both approaches, it describes all possible types of relationships among the four parameters considered and discusses the algorithmic problems related to decision tree optimization. The results presented are useful for researchers who apply decision trees and rules to algorithm design and to data analysis, especially those working in rough set theory, test theory and logical analysis of data. This book can also be used as the basis for graduate courses.
Download or read book Ethnographic Decision Tree Modeling written by Christina H. Gladwin and published by SAGE. This book was released on 1989-09 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: Why do people in a certain group behave the way they do? And, more importantly, what specific criteria was used by the group in question? This book presents a method for answering these questions.
Download or read book Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining written by Hassan AbouEisha and published by Springer. This book was released on 2018-05-22 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dynamic programming is an efficient technique for solving optimization problems. It is based on breaking the initial problem down into simpler ones and solving these sub-problems, beginning with the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. This book develops extensions of dynamic programming, enabling us to (i) describe the set of objects under consideration; (ii) perform a multi-stage optimization of objects relative to different criteria; (iii) count the number of optimal objects; (iv) find the set of Pareto optimal points for bi-criteria optimization problems; and (v) to study relationships between two criteria. It considers various applications, including optimization of decision trees and decision rule systems as algorithms for problem solving, as ways for knowledge representation, and as classifiers; optimization of element partition trees for rectangular meshes, which are used in finite element methods for solving PDEs; and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths. The results presented are useful for researchers in combinatorial optimization, data mining, knowledge discovery, machine learning, and finite element methods, especially those working in rough set theory, test theory, logical analysis of data, and PDE solvers. This book can be used as the basis for graduate courses.
Download or read book Decision and Inhibitory Trees and Rules for Decision Tables with Many valued Decisions written by Fawaz Alsolami and published by Springer. This book was released on 2019-03-13 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: The results presented here (including the assessment of a new tool – inhibitory trees) offer valuable tools for researchers in the areas of data mining, knowledge discovery, and machine learning, especially those whose work involves decision tables with many-valued decisions. The authors consider various examples of problems and corresponding decision tables with many-valued decisions, discuss the difference between decision and inhibitory trees and rules, and develop tools for their analysis and design. Applications include the study of totally optimal (optimal in relation to a number of criteria simultaneously) decision and inhibitory trees and rules; the comparison of greedy heuristics for tree and rule construction as single-criterion and bi-criteria optimization algorithms; and the development of a restricted multi-pruning approach used in classification and knowledge representation.
Download or read book Rough Sets written by Z. Pawlak and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: To-date computers are supposed to store and exploit knowledge. At least that is one of the aims of research fields such as Artificial Intelligence and Information Systems. However, the problem is to understand what knowledge means, to find ways of representing knowledge, and to specify automated machineries that can extract useful information from stored knowledge. Knowledge is something people have in their mind, and which they can express through natural language. Knowl edge is acquired not only from books, but also from observations made during experiments; in other words, from data. Changing data into knowledge is not a straightforward task. A set of data is generally disorganized, contains useless details, although it can be incomplete. Knowledge is just the opposite: organized (e.g. laying bare dependencies, or classifications), but expressed by means of a poorer language, i.e. pervaded by imprecision or even vagueness, and assuming a level of granularity. One may say that knowledge is summarized and organized data - at least the kind of knowledge that computers can store.
Download or read book Decision Trees for Decision Making written by John F. Magee and published by . This book was released on 1964 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Driven Decision Making written by Dr. Avinash S. Jagtap and published by Lulu.com. This book was released on with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Decision Trees for Decision Making written by Magee and published by . This book was released on 1964-01-01 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Decision Trees Versus Systems of Decision Rules written by Kerven Durdymyradov and published by Springer. This book was released on 2025-01-21 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores, within the framework of rough set theory, the complexity of decision trees and decision rule systems and the relationships between them for problems over information systems, for decision tables from closed classes, and for problems involving formal languages. Decision trees and systems of decision rules are widely used as means of representing knowledge, as classifiers that predict decisions for new objects, as well as algorithms for solving various problems of fault diagnosis, combinatorial optimization, etc. Decision trees and systems of decision rules are among the most interpretable models of knowledge representation and classification. Investigating the relationships between these two models is an important task in computer science. The possibilities of transforming decision rule systems into decision trees are being studied in detail. The results are useful for researchers using decision trees and decision rule systems in data analysis, especially in rough set theory, logical analysis of data, and test theory. This book is also used to create courses for graduate students.
Download or read book Rough Sets written by Sheela Ramanna and published by Springer Nature. This book was released on 2021-09-17 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume LNAI 12872 constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2021, Bratislava, Slovak Republic, in September 2021. The conference was held as a hybrid event due to the COVID-19 pandemic. The 13 full paper and 7 short papers presented were carefully reviewed and selected from 26 submissions, along with 5 invited papers. The papers are grouped in the following topical sections: core rough set models and methods, related methods and hybridization, and areas of applications.
Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Download or read book Alternating Decision Tree written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-23 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Alternating Decision Tree A categorization strategy that may be learned by machine learning is known as an alternating decision tree, or ADTree. It is connected to boosting and generalizes decision trees at the same time. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Alternating Decision Tree Chapter 2: Decision Tree Learning Chapter 3: AdaBoost Chapter 4: Random Forest Chapter 5: Gradient Boosting Chapter 6: Propositional Calculus Chapter 7: Support Vector Machine Chapter 8: Method of Analytic Tableaux Chapter 9: Boolean Satisfiability Algorithm Heuristics Chapter 10: Multiplicative Weight Update Method (II) Answering the public top questions about alternating decision tree. (III) Real world examples for the usage of alternating decision tree in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of alternating decision tree' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of alternating decision tree.
Download or read book Decision Models for Business Decision Trees written by Fernando A. Boeira and published by . This book was released on 2015-03-25 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is aimed for undergraduate students in economics, engineering, operations research, or other disciplines dealing with a branch of optimization theory: Decision Trees. It is intended to be essentially self-contained and should be suitable for classroom work or self-study. Illustrative examples are given at almost every step.Some stress is laid on the concept of a probabilistic model for the mechanism generating a set of observed data, leading to the natural application of probability theory to answer questions of interest in optimization. Decision Trees is a suitable topic for an undergraduate course but it is rarely taught.None of the material treated is original; hopefully, the order and methof of treatment will help to minimize difficulties in its use in practical problems.
Download or read book Decision Tree Statistical Learning Models An Application to New Customer Scoring written by Macià Comella Barbé and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this thesis is to explore, understand and apply statistical learning methods based on decision trees, specifically individual decision trees and bagging, random forests and gradient boosting methods. In order to do this, aresearch has been done and the theory behind each one of these methods understood.The main sources of information are thebooks "Introduction to Statistical Learning" and "The Elements of Statistical Learning" by T. Hastie, R. Tibshirani and J. Friedman.Afterwards this theory is put to practice using a real case data set and the R programming language to experiment with models of the mentioned methods. The data used comes from areal case project in which a business wishes to predict whether anew customer will be a good one based only in the information from its three first purchases.The tools used are also presented, consisting in the different R packages and functions used and its tuning parameters. The strategy used in order to obtain representative results that make possible to understand the concepts presented in the theory is explained. As well as how these results have been extracted.The sensitivity analysis has been done with the Minitab v18 software, provided by the Universitat Politècnica de Catalunya for research purposes.Finally the results are analysed. This analysis is divided in three sections.The first one is focused in a sensitivity analysis of parameters. The results show that, with the used dataset, for gradient boosting the tree depth allowed is critical to obtain a good quality of fit and prevent overfitting, andthe number of iterations allowed needs to be correctly alignedwith the learning parameter used. The results for bagging and random forests (merged as one is a particular case of the other) prove the lack of overfittingintrinsic of these modelsand discovers that if the number of variables is high and these are strongly correlated the recommended number of variables to choose at each tree node does not lead to optimum results. An initial hypothesis to guide the analysis of this fact is proposed but it is not inside the scope of the project to analyse and prove this hypothesis. The second section of the analysis consists in selecting the best performing method and apply it to the availabledataset. The gradient boosting method is chosen as the best one due to higher quality of fit obtained and a more consistent selection of variables among all scenarios. The third section compares the results obtained with gradient boosting versus the logistic regression model done by the student P. Casas in his bachelor thesis"New customers' classifier"based on the same dataset. The results show that gradient boosting performs better in terms of prediction in two of the three models created, though the difference is small, and obtains the same quality of fitin the other case. Comparing variable relevancethe most important one is shared among both methods(the total value of the purchase). Other secondary variables are shared and some of them not. Therefore it can be said there is similarity in general terms but gradient boosting and logistic regression are nottotally close between them,as it happens with the decision tree methods used in the project.
