PPT-Classification with Decision Trees and Rules

Copyright Andrew W Moore Density Estimation looking ahead Compare it against the two other major kinds of models Regressor Prediction of realvalued output Input Attributes Density Estimator. Classification. Decision-tree classification. What is classification?. What is classification?. Classification. is the task of . learning a target function . f. that maps attribute set . x. to one of the predefined class labels . Battiti. , Mauro . Brunato. .. The LION Way: Machine Learning . plus.  Intelligent Optimization. .. LIONlab. , University of Trento, Italy, . Apr 2015. http://intelligent-optimization.org/LIONbook. A . decision tree. is a graphical representation of every possible sequence of decision and random outcomes (states of nature) that can occur within a given decision making problem.. A decision tree is composed of a collection of nodes (represented by circles and squares) interconnected by branches (represented by lines).. Decision Tree. Advantages. Fast and easy to implement, Simple to understand. Modular, Re-usable. Can be learned .  . can be constructed dynamically from observations and actions in game, we will discuss this further in a future topic called ‘Learning’). Computer Game Technology. AI – Decision Trees and Rule Systems. Spring 2012. Today. AI. Decision trees. Rule-based systems. Classification. Our aim is to decide which action to take given the world state. Usman Roshan. Decision tree. From . Alpaydin. , 2010. Decision tree construction. For a given dataset there are many trees with no error. Finding the tree with no error and fewest nodes is NP-complete. Dr. Long Tran-Thanh. ltt08r@ecs.soton.ac.uk. University of Southampton. Decision Trees. Classification. Environment. Perception. Behaviour. Categorize inputs . Update belief model. Update decision making policy. Decision Trees. Gavin Brown. www.cs.man.ac.uk/~gbrown. Recap: threshold classifiers. height. weight. Q. Where is a good threshold?. 10 20 30 40 50 60. 1. 0. Also known as “decision stump”. From Decision . 2. Decision Trees and Decision Tables. Often our problem solutions require decisions to be made according to two or more conditions or combinations of conditions. Decision trees represent such decision as a sequence of steps. Object-based classifiers. Others. DECISION TREES. Non-parametric approach. Data mining tool used in many applications, not just RS. Classifies data by building rules based on image values. Rules form trees that are multi-branched with nodes and “leaves” or endpoints. 2. Decision Trees and Decision Tables. Often our problem solutions require decisions to be made according to two or more conditions or combinations of conditions. Decision trees represent such decision as a sequence of steps. Chapter 5 Divide and Conquer – Classification Using Decision Trees and Rules decision trees and rule learners two machine learning methods that make complex decisions from sets of simple choices Mirza Muhammad Waqar Expert/rule based classification 1 Contact: mirza.waqar@seecs.edu.pk Lecture Overview Image classification basics Image and feature spaces Supervised vs Unsupervised classification Decision Trees and Decision Tables 2 Decision Trees and Decision Tables Often our problem solutions require decisions to be made according to two or more conditions or combinations of conditions Decision trees represent such decision as a