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 of connectionist models inspired in the behavior of the human brain

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

6. MLP (2)
MLP provides a powerful base-learner, with advantages such as nonlinear mapping and noise tolerance,
Increasingly used 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. Resources for ppt : KDD05.ppt Weka.ppt
Weka_a_tool_for_exploratory_data_mining.ppt
...

12. Paper Abstract (keywords), Introduction,
Literature Review (journals, textbooks, scientific articles, encyclopedias),
Result (tables, graphs),
Discussions,
Conclusions,
References, ...

13. 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. (May 22, 2007). Evolution of Neural Networks for Classification and Regression. Retrieved from sciencedirect.com: science?_ob=MImg&_imagekey=B6V10-4NSWYYK-5-F&_cdi=5660&_user= &_orig=search&_coverDate=10%2F31%2F2007&_sk= &view= c&wchp=dGLbVlz-zSkWz&md5=e37e702aff003293e8fdb91aadcbf9b6&ie= /sdarticle.pdf

14. References (2)
Eibe Frank. Sourceforge.net. Retrieved November 4, 2009 from OR