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Anil Shankar Classifier Systems: Enhancing Machine Learning with Rule-Based Algorithms

Explore the components, architecture, and examples of the Classifier Systems introduced by Anil Shankar for effective machine learning. Learn about rule generation using Genetic Algorithms and the Bucket Brigade Algorithm.

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Anil Shankar Classifier Systems: Enhancing Machine Learning with Rule-Based Algorithms

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  1. Classifier Systems Anil Shankar Dept. of Computer Science University of Nevada, Reno

  2. Overview • Introduction and problem overview • Architecture • Component details • Track a specific example • Summary Anil Shankar Classifier Systems

  3. Introduction • Learning • “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” • Machine Learning, Tom Mitchell Anil Shankar Classifier Systems

  4. Problem Multiplexer Example Perfect Rule Set Anil Shankar Classifier Systems

  5. Classifier System (C.S) • Learn simple string rules in an arbitrary environment • A classifier is a simple string rule • Components • Rule and Message System • Apportionment of credit system • Genetic Algorithm Anil Shankar Classifier Systems

  6. Rule and Message System • Production system • Fixed size representation for rules • Parallel activation • Rating of a rule by an information-based economy • <message>::= { 0, 1} l • <classifier>::= <condition>:<message> • <condition>::={0, 1, #}l Anil Shankar Classifier Systems

  7. Which classifier to choose? • Bucket Brigade Algorithm • For ranking or rating individual classifiers • Classifiers buy and sell the right to trade information (information-based economy) • Auction house and clearing house • If a classifier matches a message, it participates in an auction. • The bid (B) is proportional to its strength (S) • Once activated the winner pays its bid to other classifiers which also matched the message Anil Shankar Classifier Systems

  8. Which classifier to choose?(contd…) • Notation • S = Strength • P = Payment • T = Tax • R = Reward • Cbid = Bid Coefficient • The ith classifier strength (at time step t) Si(t+1) = Si(t) – Pi(t) – Ti(t) + Ri(t) • Bid Bi = Cbid * Si • Tax Taxi = Ctax * Si • Effective Bid EBidi = Bi + N (σbid) • In terms of strength S(t+1) = S(t) – Cbid*S(t) – Ctax*S(t) + R(t) Anil Shankar Classifier Systems

  9. Generating better rules • Bucket brigade algorithm evaluates rules and decides among competing alternatives. • Use a Genetic Algorithm (GA) to generate new rules • A classifier’s strength (S) is used as its fitness • Similar to the simple genetic algorithm • Entire population is not replaced at the next generation (Generation gap ) • GA period (epoch) • Number of time steps between GA calls • Time step = rule-message cycle • Crowding to maintain diversity • Mutation over a ternary alphabet {1, 0, # } Anil Shankar Classifier Systems

  10. Generating better rules • Selection is performed using roulette-wheel selection • The GA is run according every GA Period or when conditioned on particular events (lack of match or poor performance) Anil Shankar Classifier Systems

  11. T= 0 C.S in action (1) Strength (S) Messages (Msg) Match (M) Bid (B) CBid = 0.1 CTax = 0.0 Anil Shankar Classifier Systems

  12. C.S in action (2) T= 1 Strength (S) Messages (Msg) Match (M) Bid (B) CBid = 0.1 CTax = 0.0 Anil Shankar Classifier Systems

  13. C.S in action (3) T= 2 Strength (S) Messages (Msg) Match (M) Bid (B) CBid = 0.1 CTax = 0.0 Anil Shankar Classifier Systems

  14. C.S in action (4) T= 3 Strength (S) Messages (Msg) Match (M) Bid (B) CBid = 0.1 CTax = 0.0 Anil Shankar Classifier Systems

  15. C.S in action (5) T= 4 Strength (S) Messages (Msg) Match (M) Bid (B) CBid = 0.1 CTax = 0.0 Anil Shankar Classifier Systems

  16. C.S in action (6) T= 5 Strength (S) CBid = 0.1 CTax = 0.0 Anil Shankar Classifier Systems

  17. Are these rule-sets the same? Anil Shankar Classifier Systems

  18. Multiplexer Example • Default Hierarchy • General rules cover general conditions and specific rules cover exceptions • Parsimony • Fewer rules • Enlargement of the solution set • While the problem space remains the same Anil Shankar Classifier Systems

  19. Summary • A classifier is a simple string rule • Classifier System • rule-message system, • apportionment of credit mechanism • GA • Advantages of CS • rules are simple • use fixed length representation • parallel activation • operate in an information-based economy Anil Shankar Classifier Systems

  20. Thank You Questions ? Anil Shankar Classifier Systems

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