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  1. 1. Introduction to Machine Learning Lecture 19 Genetic Fuzzy Systems Albert Orriols i Puig http://www.albertorriols.net htt // lb t i l t aorriols@salle.url.edu Artificial Intelligence – Machine Learning g g Enginyeria i Arquitectura La Salle Universitat Ramon Llull
  2. 2. Recap of Lectures 5-18 Supervised learning p g Data classification Labeled data Build a model that covers all the space Unsupervised Uns per ised learning Clustering Unlabeled data Group similar objects Association rule analysis Unlabeled data Get the most frequent/important associations Slide 2 Artificial Intelligence Machine Learning
  3. 3. Today’s Agenda Fuzzy Logics Fuzzy Systems Genetic Fuzzy Systems Slide 3 Artificial Intelligence Machine Learning
  4. 4. Fuzzy Logics Looking up in the dictionary… gp y Fuzzy = “not clear, distinct, or precise; blurred” The Th world is imprecise, not clear, blurred… ld i i i tl bl d The world is fuzzy! Definition of fuzzy logics yg A form of knowledge representation suitable for notions that cannot be defined precisely, but which depend upon their p y, p p contexts Let’s go from true and false (traditional logics) to something more powerful Slide 4 Artificial Intelligence Machine Learning
  5. 5. Fuzzy Logics Traditional logic representation g p Slow Fast Logic rep Slow speed = 0 Fast speed = 1 Slide 5 Artificial Intelligence Machine Learning
  6. 6. Fuzzy Logics How fast is fast? Definition of slow and fast depend on the eyes of the beholder Natural language contains many subjective t N t ll ti bj ti terms How can I deal with this? H d l ith thi ? Fast Very fast Very slow Slow These four are linguistic terms St , Still, I need to de e t e se a t cs o eac eed define the semantics of each linguistic term! Slide 6 Artificial Intelligence Machine Learning
  7. 7. Fuzzy Logics Classical view Define intervals: very slow [0 – 0.25] slow [0.25 – 0.5] fast [0.5 – 0.75] very fast [0.75 – 1] Fuzzy logics view yg Consider the degree with which each observation belongs to each linguistic term Define a membership function Slide 7 Artificial Intelligence Machine Learning
  8. 8. Fuzzy Logics Member ship functions Semantics of the system Fast Very fast Very slow Slow 0 0.25 0.50 0.75 1 Slide 8 Artificial Intelligence Machine Learning
  9. 9. Fuzzy Logics Many different membership functions. Some of them are y p 1 c ab d Slide 9 Artificial Intelligence Machine Learning
  10. 10. Fuzzy Systems Fuzzy systems yy are fundamental methodologies to represent and process gu s c o a o linguistic information use fuzzy logic to either represent the knowledge or model the interactions a d relationships a o g the sys e variables in e ac o s and e a o s ps among e system a ab es environments where there is uncertainty and imprecision. E.g. of knowledge representation: If john is tall and fast then strong Genetic fuzzy systems yy The use of genetic/evolutionary algorithms (GAs) to design fuzzy systems yy Slide 10 Artificial Intelligence Machine Learning
  11. 11. GFS Slide 11 Artificial Intelligence Machine Learning
  12. 12. GFS Two key elements: y Fuzzy system In our case, we will focus on rule-based systems case Genetic algorithm Fuzzy system yy Slide 12 Artificial Intelligence Machine Learning
  13. 13. Fuzzy Rule-Based Systems Rule base If size is small and weight is small then quality is bad If size is small and weight i l ii ll d i ht is large th quality i medium then lit is di If size is large and weight is small then quality is medium If size is large and weight is large then quality is good Data base Slide 13 Artificial Intelligence Machine Learning
  14. 14. Fuzzy Rule-Based Systems Operation of the inference system Centre of gravity Slide 14 Artificial Intelligence Machine Learning
  15. 15. Fuzzy Rule-Based Systems Great, I know how to infer… But who gives me , g The rules The i f Th information of the data base (the semantics) ti f th d t b (th ti ) The inference engine Inference system Defuzzification methods Use a genetic algorithm for this task Slide 15 Artificial Intelligence Machine Learning
  16. 16. Recall GAs? Population Individual 1 Fit. 1 Individual 2 Fit. 2 Individual i Population ... ... Individual n Fit. n Individual j Individual 1 Initialization Individual 2 Individual 1 ... Individual n Individual n Individual i’ Individual i’’ Mutation Individual j’ Individual j’’ Individual I di id l 1’ Individual 1’’ Individual n’ Individual n’’ Selection + Mutation: Continuous improvement and local search Selection + Recombination: Innovation Slide 16 Artificial Intelligence Machine Learning
  17. 17. Where Do we Use the GA? Taxonomy of GFS ( y (Herrera, 2008) , ) Slide 17 Artificial Intelligence Machine Learning
  18. 18. Where Do we Use the GA? Taxonomy of GFS ( y (Herrera, 2008) , ) Slide 18 Artificial Intelligence Machine Learning
  19. 19. Topics We are going to see g g Genetic tuning 1. Genetic rule learning G ti ll i 2. Genetic rule selection 3. Genetic DB learning 4. S u ta eous genetic ea Simultaneous ge et c learning o KB co po e ts g of components 5 5. Genetic learning of KB components and inference engine 6. pa a ete s parameters 1st seen i thi l t in this lecture. 2nd-5th seen i next l t 5 in t lecture Information based on the paper Herrera (2009) and the corresponding presentation di t ti Slide 19 Artificial Intelligence Machine Learning
  20. 20. 1. Genetic Tuning Typically membership functions yp y p are defined by domain experts are j t selected f just l t d from general f l forms: triangles, t ti l trapezoids, id Gaussian… But, B t could we have better membership functions? ld h b tt b hi f ti ? Let a GA tune the membership functions Also, tune the inference parameters Slide 20 Artificial Intelligence Machine Learning
  21. 21. 1. Genetic Tuning How do we apply the GA? So, we are modifying the partitions of the feature space Slide 21 Artificial Intelligence Machine Learning
  22. 22. 1. Genetic Tuning An example: Tuning triangular membership functions p g g p Each chromosome encodes a different DB definition 2 vars x 3 ling. labels = 6 mem. functions g Triangles 3 real values to code them Chromosome length = 18 genes Note that the RB remains unchanged! Slide 22 Artificial Intelligence Machine Learning
  23. 23. Next Class Next l N t class Genetic rule learning 1. Genetic rule selection 2. Genetic DB learning 3. Simultaneous genetic learning of KB components 4. Genetic learning of KB components and inference engine G ti l i f t di f i 5. parameters Applications Slide 23 Artificial Intelligence Machine Learning
  24. 24. Introduction to Machine Learning Lecture 19 Genetic Fuzzy Systems Albert Orriols i Puig http://www.albertorriols.net htt // lb t i l t aorriols@salle.url.edu Artificial Intelligence – Machine Learning g g Enginyeria i Arquitectura La Salle Universitat Ramon Llull