Artificial Neural Network Building Using WEKA Software - PowerPoint PPT Presentation

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Artificial Neural Network Building Using WEKA Software

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  1. Artificial Neural Network Building Using WEKA Software Arief Rakhman Goeij Yong Sun Rama Catur

  2. Outline Cloud • ANN • MLP • WEKA • WEKA Main Features • MLP in WEKA • Practice!

  3. ANN (Artificial Neural Network)a set ofconnectionist models inspired in the behavior of the humanbrain

  4. ANN (2) • Artificial Neural Network is a mathematical model or computational model that tries to simulate the structure and/or functional aspects of biological neural networks. • ANN consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation [10]. • ANN is an adaptive system that can change structures itself based information that affect the process during computation of connectioning approach. • ANN is kind of non-linear statistical data modeling tool. It usually used with complex model or to find pattern of data.

  5. x1 xn MLP (Multilayer Perceptron)themost popular ANN architecture, where neurons aregrouped in layers and only forward connections exist[1] weight perceptron output input hidden layer

  6. MLP (2) • MLP provides a powerful base-learner, with advantagessuch as nonlinear mapping and noise tolerance, • Increasinglyused in Data Mining due to its good behavior in terms of predictive knowledge [2]

  7. WEKA • A kind of bird in Hamilton, New Zealand • Waikato Environment for Knowledge Analysis • Collection of machine learning algorithms and data processing tools implemented in Java. Released under the GPL • Have been developed since 1993 • Support for the whole process of experimental data mining : • Preparation of input data • Statistical evaluation of learning schemes • Visualization of input data and the result of learning • Used for education, research and applications

  8. WEKA Main Features • 49 data preprocessing tools • 76 classification/regression algorithms (including MLP) • 8 clustering algorithms • 15 attribute/subset evaluators + 10 search algorithms for feature selection • 3 algorithms for finding association rules • 3 graphical user interfaces • “The Explorer” (exploratory data analysis) • “The Experimenter” (experimental environment) • “The KnowledgeFlow”(new process model interface)

  9. MLP in WEKA • A Classifier function that uses backpropagation algoritm to classify instances • The network can also be monitored and modified during training time • The nodes in this network are all sigmoid (except for when the class is numeric in which case the the output nodes become unthresholded linear units)

  10. Practice • Let’s learn by doing!

  11. References [1]A Abraham.(2004). Meta learning evolutionary artificial neural networks. In Neurocomputing 56 (p. 1–38). [2]D.H. Ackley, M.L. Littman. (1994). A case for Lamarckian Evolution. MA: Addison-Wesley (p. 3–10) [3] Rochaa, M., Cortezb, P., & Nevesa, J. (May22, 2007). Evolution of NeuralNetworks for Classification and Regression. Retrieved from sciencedirect.com: http://www.sciencedirect.com/ science?_ob=MImg&_imagekey=B6V10-4NSWYYK-5-F&_cdi=5660&_user= 8487756&_orig=search&_coverDate=10%2F31%2F2007&_sk=999299983&view= c&wchp=dGLbVlz-zSkWz&md5=e37e702aff003293e8fdb91aadcbf9b6&ie= /sdarticle.pdf

  12. References (2) Eibe Frank. Sourceforge.net. Retrieved November 4, 2009 from http://prdownloads.sourceforge.net/weka/weka.ppt OR https://sourceforge.net/projects/weka/files/documentation/Initial%20upload%20and%20presentations/weka.ppt/download