Decision tree

About: Decision tree is a research topic. Over the lifetime, 26193 publications have been published within this topic receiving 588185 citations.

Induction of Decision Trees

Abstract: The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.

Programs for Machine Learning

Abstract: Algorithms for constructing decision trees are among the most well known and widely used of all machine learning methods. Among decision tree algorithms, J. Ross Quinlan's ID3 and its successor, C4.5, are probably the most popular in the machine learning community. These algorithms and variations on them have been the subject of numerous research papers since Quinlan introduced ID3. Until recently, most researchers looking for an introduction to decision trees turned to Quinlan's seminal 1986 Machine Learning journal article [Quinlan, 1986]. In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments. As such, this book will be a welcome addition to the library of many researchers and students.

Experiments with a new boosting algorithm

Abstract: In an earlier paper, we introduced a new "boosting" algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that con- sistently generates classifiers whose performance is a little better than random guessing. We also introduced the related notion of a "pseudo-loss" which is a method for forcing a learning algorithm of multi-label concepts to concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems. We performed two sets of experiments. The first set compared boosting to Breiman's "bagging" method when used to aggregate various classifiers (including decision trees and single attribute- value tests). We compared the performance of the two methods on a collection of machine-learning benchmarks. In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem.

Introduction to Data Mining

Abstract: 1 Introduction 1.1 What is Data Mining? 1.2 Motivating Challenges 1.3 The Origins of Data Mining 1.4 Data Mining Tasks 1.5 Scope and Organization of the Book 1.6 Bibliographic Notes 1.7 Exercises 2 Data 2.1 Types of Data 2.2 Data Quality 2.3 Data Preprocessing 2.4 Measures of Similarity and Dissimilarity 2.5 Bibliographic Notes 2.6 Exercises 3 Exploring Data 3.1 The Iris Data Set 3.2 Summary Statistics 3.3 Visualization 3.4 OLAP and Multidimensional Data Analysis 3.5 Bibliographic Notes 3.6 Exercises 4 Classification: Basic Concepts, Decision Trees, and Model Evaluation 4.1 Preliminaries 4.2 General Approach to Solving a Classification Problem 4.3 Decision Tree Induction 4.4 Model Overfitting 4.5 Evaluating the Performance of a Classifier 4.6 Methods for Comparing Classifiers 4.7 Bibliographic Notes 4.8 Exercises 5 Classification: Alternative Techniques 5.1 Rule-Based Classifier 5.2 Nearest-Neighbor Classifiers 5.3 Bayesian Classifiers 5.4 Artificial Neural Network (ANN) 5.5 Support Vector Machine (SVM) 5.6 Ensemble Methods 5.7 Class Imbalance Problem 5.8 Multiclass Problem 5.9 Bibliographic Notes 5.10 Exercises 6 Association Analysis: Basic Concepts and Algorithms 6.1 Problem Definition 6.2 Frequent Itemset Generation 6.3 Rule Generation 6.4 Compact Representation of Frequent Itemsets 6.5 Alternative Methods for Generating Frequent Itemsets 6.6 FP-Growth Algorithm 6.7 Evaluation of Association Patterns 6.8 Effect of Skewed Support Distribution 6.9 Bibliographic Notes 6.10 Exercises 7 Association Analysis: Advanced Concepts 7.1 Handling Categorical Attributes 7.2 Handling Continuous Attributes 7.3 Handling a Concept Hierarchy 7.4 Sequential Patterns 7.5 Subgraph Patterns 7.6 Infrequent Patterns 7.7 Bibliographic Notes 7.8 Exercises 8 Cluster Analysis: Basic Concepts and Algorithms 8.1 Overview 8.2 K-means 8.3 Agglomerative Hierarchical Clustering 8.4 DBSCAN 8.5 Cluster Evaluation 8.6 Bibliographic Notes 8.7 Exercises 9 Cluster Analysis: Additional Issues and Algorithms 9.1 Characteristics of Data, Clusters, and Clustering Algorithms 9.2 Prototype-Based Clustering 9.3 Density-Based Clustering 9.4 Graph-Based Clustering 9.5 Scalable Clustering Algorithms 9.6 Which Clustering Algorithm? 9.7 Bibliographic Notes 9.8 Exercises 10 Anomaly Detection 10.1 Preliminaries 10.2 Statistical Approaches 10.3 Proximity-Based Outlier Detection 10.4 Density-Based Outlier Detection 10.5 Clustering-Based Techniques 10.6 Bibliographic Notes 10.7 Exercises Appendix A Linear Algebra Appendix B Dimensionality Reduction Appendix C Probability and Statistics Appendix D Regression Appendix E Optimization Author Index Subject Index