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Artificial Neural Network Building Using WEKA Software

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## Artificial Neural Network Building Using WEKA Software

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**Artificial Neural Network Building Using WEKA Software**Arief Rakhman Goeij Yong Sun Rama Catur**Outline Cloud**• ANN • MLP • WEKA • WEKA Main Features • MLP in WEKA • Practice!**ANN**(Artificial Neural Network)a set ofconnectionist models inspired in the behavior of the humanbrain**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.**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**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]**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**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)**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)**Practice**• Let’s learn by doing!**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**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