Alanoud alqoufi inductive learning

Inductive learning

1. Inductive learning Presented by:Alanoud Saad Alqoufi ID number: 435920068 Supervised by: Prof.Mehmet Aksoy Second semester 20/04/2015 KSU-Information System

2. Outline Learning Machine Learning Inductive Learning Why Inductive Learning Inductive Learning Methods Rules Family Of Algorithms Application of inductive learning

3. Learning An agent is learning if it improves its performance on future tasks after making observations about the world. [ 1 ] A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E . [2]

4. Why would agent learn? 1. The designers cannot anticipate all possible situations that the agent might find itself in 2. The designers cannot anticipate all changes over time 3. Sometimes human programmers have no idea how to program a solution themselves

5. Machine Learning Machine learning is concerned with computer programs that automatically improve their performance through experience. [1 ] Field of study that gives computers the ability to learn without being explicitly programmed. [3]

6. Traditional Programming Machine Learning Computer Data Program Output Computer Data Output Program Machine Learning

7. Inductive Learning A new field of machine learning known as inductive learning has been introduced to help in inducing general rules and predicting future activities . [ 4] Inductive learning is learning from observation and earlier knowledge by generalization of rules and conclusions. Inductive learning allows for the identification of training data or earlier knowledge patterns. [5] The identified and extracted generalized rules come to use in reasoning and problem solving . [6]

8. I nductive learning • Given examples of a function (X, F(X)) • Predict function F(X) for new examples X

9. Inductive learning Inductive : This cat is black. That cat is black A third cat is black. Therefore all cats are black Deductive: Bachelor's are unmarried men. Bill is unmarried. Therefore, Bill is a bachelor

10. Why inductive learning Alternative method of knowledge acquisition in which knowledge learned or induced from examples Human experts are capable of using their knowledge in their daily work, but they usually cannot summaries and generalize their knowledge explicitly in a form which is sufficiently systematic, correct and complete for machine representation and application . [7] While it is very difficult for an expert to articulate his knowledge, it is relatively easy to document case studies of the expert's skills at work. [7]

11. Inductive Learning Methods 1-Divide-and- conquer CLS ID3 C4.5 2-Covering AQ family CN2 RULES A set of training examples usually used to form a decision tree . [5]

12. Rules Family Of Algorithms Rule1 Pham and Aksoy have developed RULES-1 ( RULe Extraction System-1) . [8] The first member of RULES family of algorithms RULES-1 . [9] Extracts rules for objects in similar sets of classes Each object has its own attributes and values The attributes and the values associated with them in a collection of objects form an array of attributes and values Total number of elements of the array is the total number of all possible values EX: 4 attributes with values ( 3,4,2,5 ). Total number of elements is 14

13. Rules Family Of Algorithms Rule1 Example: Attributes: Weather, Temperature Values : {rainy, sunny, snowy} and {low, average, high} Attribute-value pairs : ( weather,rainy ),( weather,sunny ),( weather,snowy ),( temperature,low ),( temperature,average ),( temperature,High )

14. Rules Family Of Algorithms

15. Rules Family Of Algorithms Rules versions: Rule-1 Rule-2 Rule-3 Rule-3 plus Rule-4 Rule-5 Each version has some extra new features to overcome some problems that cannot be coped with using previous versions. The algorithms have been used for many application which shows their good performance . [ 8]

16. Application of inductive learning The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications . [10] Inductive learning algorithms are domain independent and can be used in any task involving classification or pattern recognition . [ 11] Making Credit Decisions Education Medical applications

17. References [1] S.R. RUSSELL and P.Norvig , “Artificial Intelligence: A Modern Approach” , 3d.ed. USA , Prentice Hall,2009 , pp. 693-767. [2] slide2[2] T. M. Mitchell , “Machine Learning ” ,1st.ed . U.K, McGraw-Hill , 1997,pp. 1-5. [3] A.L.Samuel , ”Some Studies in Machine Learning Using the Game of Checkers” , vol.11 ,  no.6,pp. 601 - 617,Nov. 1967 . [4] H . A. ELGIBREEN and M. S. AKSOY , “ RULES – TL : A SIMPLE AND IMPROVED RULES ” , J . Theor . Appl. Inf. Technol. , vol. 47, no. 1, 2013 . [5] A. M. AlMana and M . S. AKSOY, “An Overview of Inductive Learning Algorithms” ,” Int. J. Comput . Appl. , vol. 88 – no.4 , 2014. [6] A. H. Mohamed and M. H. S. Bin Jahabar , “Implementation and Comparison of Inductive Learning Algorithms on Timetabling”, Int. J. Inf. Technol. , vol. 12, no. 7, pp. 97–113, 2006. [7 ] M.S.Aksoy “A review of inductive learning algorithms” , Journal of Faculty of Management, Istanbul, Vol.25, No.2, pp.171-186, Turkey, 1996. [8] M. S. Aksoy , “A Review of RULES Family of Algorithms” , Math . Comput . Appl., vol. 13, no. 1, pp. 51–60, 2008. [9] D.T.Pham and M.S.Aksoy , “RULES: A simple rule extraction system” , Expert Systems with Applications, Vol.8, No.1, pp.59-65, USA, 1995. [10] J.R. Quinlan , ”Induction of Decision Trees” , vol 1, Centre for Advanced Computing Sciences, New South Wales Institute of Technology, Sydney , pp 81-106 ,2007. [11] M. S. Aksoy , A. Almudimigh , O. Torkul , and I. H. Cedimoglu , “Applications of Inductive Learning to Automated Visual Inspection”, Int. J. Comput . Appl., vol. 60, no. 14, pp. 14–18, 2012.

18. Thank You