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Meta Learning and Active Learning: Collaborative Knowledge Discovery in Distributed Systems

Meta Learning and Active Learning: Collaborative Knowledge Discovery in Distributed Systems. A+B=?. Dr Yonghong Peng Department of Computing School of Informatics University of Bradford. Dr Yonghong Peng , Department of Computing, School of Informatics, University of Bradford.

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Meta Learning and Active Learning: Collaborative Knowledge Discovery in Distributed Systems

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  1. Meta Learning and Active Learning:Collaborative Knowledge Discovery in Distributed Systems A+B=? Dr Yonghong Peng Department of Computing School of Informatics University of Bradford

  2. Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Meta Learning and Active Learning:Collaborative Knowledge Discovery in Distributed Systems • Knowledge Communication in Distributed Systems; • Knowledge Discovery/Management. • Meta-Learning and Active Learning; • CKD framework and Key Techniques.

  3. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Systems are toward distributed • Centralized Systems: Their actions are coordinated based on the communicating with one control centre. • This kind of centralized communication is usually less efficient.

  4. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Systems are toward distributed • Decentralized Systems: all component are autonomous, and their actions are coordinated based on their communication. • Free of central control.

  5. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Characteristics of Distributed Systems • Their effectiveness and efficiency rely on the capability of collaboration among the components/agents. • The capability of collaboration comes from the ability of communication between all the components.

  6. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Issues in Distributed Systems Data Communication: • Data communication is time-consuming and expensive. • Data collected from a variety of sources are always heterogeneous. Instead of using data communication, knowledge sharing is the key for a successful distributed system. An new concept is called knowledge mobility.

  7. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Communication via Data or Knowledge This is what you need! It is A not B, and A is bigger than B 20%. This is what I have! I do not know what it is. Communication via Data Communication via Knowledge

  8. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Knowledge is not easy to obtain • Data is available everywhere but is difficult to use; • Data is easy to collect: availability of large amount of data. • Knowledge is easy to use but is difficult to obtain. • Shortage of domain experts; • Knowledge obtained from different experts may be Inconsistent.

  9. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Knowledge Discovery and Management • Strategies • Knowledge Discovery: • To extract knowledge from data using machine learning and data mining techniques. • knowledge management: to Make use of knowledge effectively • Knowledge Verification • Knowledge Updating; • Knowledge Reuse.

  10. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Knowledge Discovery and Management Drawback of the current techniques: -- They are inefficient as they are working in a passive style! • Data Mining: we do not know what we will get. • No interaction between the existing knowledge of upcoming mining activities. • Knowledge management are performed after the knowledge has been extracted, not within the process of learning.

  11. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Collaborative Knowledge Discovery (CKD) New Strategies: Managing the knowledge when learning. • Collaborative Learning: • ----- To learn how to work with others • Learn according to what I want to know. • Learn according to what the partners want to know. Idea: Meta-Learning and Active Learning based Collaborative Knowledge Discovery.

  12. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Meta-Learning and Active Learning What is Meta-Learning? • Meta-learning is to learn how a learner works. Applications: • To select the suitable algorithm(s) (Current); • MetaL European Project (www.metal-kdd.org) • To learn the collaborative knowledge (NEW).

  13. Collaborative Knowledge Discovery in Distributed Systems Application Objectives Expected Performances Meta-Learner Data Characterisation Model/Algorithm Characterisation Knowledge generation Data Collection Data pre-processing Machine Learner Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Meta-Learning Process End-users Meta-Data Meta-Knowledge

  14. Collaborative Knowledge Discovery in Distributed Systems Ranking ALs LA1; LA3; …… Ranking LAs AL2; AL1; …… New Data Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Meta-Learning for Algorithm Selection [LA1,f1,f2,……….., Acc1, Time1] …………. [LAn,f1,f2,……….., Acc_n, Time_n] …………. Meta-Knowledge Data Characterisations Ranker Pre-processing Machine Learner Post- processing

  15. Collaborative Knowledge Discovery in Distributed Systems Data Miner Data Knowledge Active Learner Data DM Knowledge Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Active Learning What is Active-Learning? Passive Learning: Data-Driven learning: Active Learning: Objective-Driven learning:

  16. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Active Learning • Tasks of Active Learning: keep on ansowering • What can I learn? • How to Learn? • Active Learning Process:

  17. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Meta-Learning based Active Learning • Approaches: • what I can learn from the data: • Data Characteristic Techniques (DCT); • Perform the rough mining (RM) with the sampled data; • Select appropriate methods: • Select the target models according to the objectives; • Using Meta-Learning to select the learning methods.

  18. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Data Characterisation Techniques (DCT) • StatLog type DCT: • Simple Measures (e.g. number of attributes, classes et al.) • Statistical Measures (e.g. mean of numerical attributes) • Information-based measures (e.g. entropy of classes) • Histograms based DCT • information regarding the distribution of values of attributes with relational nature (e.g. mutual information between symbolic attributes and class) • Landmarking • use the performance of simple (fast) learners to predict the performance of candidate algorithms

  19. Collaborative Knowledge Discovery in Distributed Systems x1 x2 x3 x2 x4 C1 C3 x4 x5 C1 C4 C1 C2 C3 C1 Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. New Data Characterisation Techniques • Idea: • Capturing information from the Standard Decision Tree model (or other models) [Peng, IDDM2002, DS2002]. • Approach: • using standard decision tree method: C5.0 • measuring the size, structure and shape of tree.

  20. Collaborative Knowledge Discovery in Distributed Systems 1. 2. 3. C5.0  13%, 30s Ltree  8%, 35s … Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Meta-Learning for Algorithm Selection • Given new data set • 1.characterize it • general (# attributes, # examples, ...) • statistical (skewness, kurtosis, ...) • information-theoretic (class entropy, ...) Adversor System: www.metal-kdd.org 2. select k neighbors • 3. retrieve performanceinformation • accuracy + time 4. Rank LAs according to the accuracy and time.

  21. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Collaborative Knowledge Discovery Framework Application of Meta-Learning and Active Learning: 1) Control the process of each learning agent actively. 2) Coordinate activities of multiple learning agents. 3) Synthesize the outcome of distributed learning agents.

  22. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Collaborative Knowledge Discovery Framework • Key techniques in collaborative learning: • To improve my situation • What do I need? • Where can I get it? • How to get it? • To improve the partners’ situation • What do other need? • Do I possibly have? • How can I get it efficiently? – Objective driven problem solving; – On-line information retrieval; – Meta-learning and Meta-knowledge; – Knowledge communication; – On-line DCT or data probe; – Meta-learning and meta-knowledge.

  23. Collaborative Knowledge Discovery in Distributed Systems Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Collaborative Knowledge Discovery- Summary • Knolwedge discovery deals with extracting interesting associations, classifiers, clusters and patterns, which are previous unknown, from data; • The emergence of network-based computing has introduced a new dimension to this problem, i.e., the distributed sources of data and computing. • Advanced analysis of distributed data for extracting useful knowledge is the next natural step. • The existing data mining algorithms are designed to work for centralized data, and they often do not pay attention to the distributed resource. • Collaborative Knowledge Discovery is a new strategy and approach.

  24. Dr Yonghong Peng, Department of Computing, School of Informatics, University of Bradford. Meta Learning and Active Learning:Collaborative Knowledge Discovery in Distributed Systems Thanks! y.h.peng@bradford.ac.uk www.inf.brad.ac.uk/~yhpeng

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