1. Neural networks – Hands on
Delta rule and Backpropagation algorithm
MetaNeural format for predictive data mining
Iris Data
Magnetocardiogram data

2. Neural net yields weights to map inputs to outputs
Neural Network
Molecular
weight
w11 h
w11  
Boiling Point
H-bonding  
Biological response
Hydrofobicity  h
Electrostatic
interactions
w23 
w34
Observable
Projection
Molecular Descriptor
There are many algorithms that can
determine the weights for ANNs
RENSSELAER

3. McCulloch-Pitts neurony x 1 3 N S
f() w 2
RENSSELAER

4. Neural network as collection of M-P neuronsx 1 2 w 11 12 13 23 22 32 21 3 y
First hidden
layer
Second hidden
Output
neuron
RENSSELAER

5. Standard Data Mining Terminology
Basic Terminology
- MetaNeural Format
- Descriptors, features, response (or activity) and ID
- Classification versus regression
- Modeling/Feature detection
- Training/Validation/Calibration
- Vertical and horizontal view of data
Outliers, rare events and minority classes
Data Preparation
- Data cleansing
- Scaling
Leave-one-out and leave-several-out validation
Confusion matrix and ROC curves

6. Standard Data Mining Terminology
Basic Terminology
- MetaNeural Format
- Descriptors, features, response (or activity) and ID
- Classification versus regression
- Modeling/Feature detection
- Training/Validation/Calibration
- Vertical and horizontal view of data
Outliers, rare events and minority classes
Data Preparation
- Data cleansing
- Scaling
Leave-one-out and leave-several-out validation
Confusion matrix and ROC curves

7. TERMINOLOGY Demo: iris_view.bat Standard Data Mining Problem
Header and Data
MetaNeural Format
- descriptors and/or features
- response (or activity to predict)
- pattern ID
- data matrix
Validation/Calibration
Training/Validation/Test Set
Demo: iris_view.bat

9. UC URVINE DATA REPOSITORY
Datafile Name: Fisher's Iris
Datafile Subjects: Agriculture , Famous datasets
Description: This is a dataset made famous by Fisher, who used it to illustrate principles of discriminant analysis. It contains 6 variables with 150 observations.
Reference: Fisher, R. A. (1936). The Use of Multiple Measurements in Axonomic Problems. Annals of Eugenics 7,
Story Names: Fisher's Irises
Authorization: free use
Number of cases: 150
Variable Names:
1.Species_No: Flower species as a code
2.Species_Name: Species name
3.Petal_Width: Petal Width
4.Petal_Length: Petal Length
5.Sepal_Width: Sepal Width
6.Sepal_Length: Sepal Length

11. S • ANALYZE code has neural networks modules built-in • Either run:
analyze root.pat 4331 (single training and testing)
analyze root.pat 4332 (LOO)
analyze root.txt 4333 (bootstrap mode)
• Results for analyze are in resultss.xxx and resultss.ttt
• Note that patterns have to be properly scaled first
• The file name meta overrides the default input file for analyze S

12. Neural Network Module in Analyze Code
ROOT
ROOT.PAT
ROOT.TES
(ROOT.WGT)
(ROOT.FWT)
(ROOT.DBD)
Use Analyze root 4331 for easy way
(the file meta let you override defaults)
Analyze
resultss.XXX
resultss.TTT
ROOT.TRN
(ROOT.DBD)
ROOT.WGT
ROOT.FWT

13. MetaNeural Input File for the ROOT Generating and Scaling Data
=> 4 layers
2 => 2 inputs
16 => # hidden neurons in layer #1
4 => # hidden neurons in layer# 2
1 => # outputs
300 => epoch length (hint:always use 1, for the entire batch)
0.01 => learning parameters by weight layer (hint: 1/# patterns or 1/# epochs)
0.01
=> momentum parameters by weight layer (hint use 0.5)
0.5
=> some very large number of training epochs
=> error display refresh rate
=>sigmoid transfer function
=> Temperature of sigmoid
check.pat => name of file with training patterns (test patterns in root.tes)
=> not used (legacy entry)
=> not used (legacy entry)
=> exit training if error < 0.02
=> initial weights from a flat random distribution
=> initial random weights all fall between –2 and +2

14. Generating and Scaling Iris Data

15. Run Neural Net for Iris Data