MetaNeural Data Mining with Neural Networks

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

4. 1 w 2 S w x Output f() 11 11 1 neuron 1 w 3 S w f() 12 11 y 1 w 13 S S f() x f() 2 1 w 22 S 3 w f() 1 21 w 23 S 2 w f() 32 Second hidden layer First hidden layer Neural network as collection of M-P neurons 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 • 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, 179-188. 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 S S S 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

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 => 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 0.01 0.5 => momentum parameters by weight layer (hint use 0.5) 0.5 0.5 10000000 => some very large number of training epochs 200 => error display refresh rate 1 =>sigmoid transfer function 1 => Temperature of sigmoid check.pat => name of file with training patterns (test patterns in root.tes) 0 => not used (legacy entry) 100 => not used (legacy entry) 0.02000 => exit training if error < 0.02 0 => initial weights from a flat random distribution 0.2 => initial random weights all fall between –2 and +2

14. Generating and Scaling Iris Data

15. Run Neural Net for Iris Data