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Waikato Environment for Knowledge Analysis

Presentation By : Mr.Wassanun Sangjun [Nick]

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CS@KMUTNB

WEKA

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Topics in presentation today

1) Explorer Application1.1) How to use data ? - 1.2) Preprocess Tab 1.3) Classify Tab -

2) Classify Tab2.1) Test Option - 42.2) Classifier Output

3) Multilayer Perceptron [MLP]3.1) Principle of MLP MLP

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Explorer Application

How to use data ? - WEKA

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Explorer

Application Explorer Open file

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Explorer Application

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.CSV .ARFF

.CSV MS Excel

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Explorer Application

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@attribute column name datatype

- Numeric - Nominal String , DateTime

@relation name

@data attribute

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Explorer Application

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Preprocess Tab Maximum , Minimum , Mean , SD

Classify Tab

attribute

attribute

Maximum , Minimum , Mean , SD attribute attribute

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Classify Tab

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Classify Tab

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Classify Tab

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Multilayer Perceptron

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(Correlation Coefficient)

(Mean Absolute Error)

attribute numeric nominal

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Classify Tab

Test Option 4

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1) Use Training Set Use all data to train and test.

2) Supplied test set Can separate data for train and test by yourself.

3) Cross validation folds Separate all data into number of groupthen leave one out for test and the other for train. Repeat it untilall of datas group being tested.Example : Have data 3 groups (3 cycles) , so the cycle as follows.3.1) group 1 for test , group 2,3 for train.3.2) group 2 for test , group 1,3 for train.

3.3) group 3 for test , group 1,2 for train.

4) Percentage split Data sampling as a percentage.Example : 25% means 25% for train and 75% for test.

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Multilayer Perceptron [MLP]

Multilayer Perceptron ()

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Multilayer Perceptron [MLP]

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1) Input Layer -1 1

2) Hidden Layer weight (neurons) hidden layer

1 input

3) Output Layer

4) Neurons