Categorization using Neural Networks by Fatai Salami (Phatye)

● Fatai Salami ● Software Developer – Lecturer – Researcher ● [email protected] or @phatye Profile

● Sometimes referred to as classification ● categorization is assigning a category ci to document di What is Categorization

● text categorization (e.g., spam filtering) ● fraud detection ● optical character recognition ● machine vision (e.g., face detection) ● natural-language processing (e.g., spoken language understanding) ● market segmentation (e.g.: predict if customer will respond to promotion) ● Bioinformatics (e.g., classify proteins according to their function) Categorization Tasks

● IT DEPENDS ● How large is your training set?, How large is your feature space? Data Set linearly separable? ● Quality data trumps everything What Algorithm should I use?

Case Study: Will s/he run away with my money?

● Clean data ● Identify possible features ● Separate data in the ratio 60 (train) : 20 (validate) : 20 (test) Getting data

● Organization ● State of residence ● Area of residence ● Monthly income ● Gender ● Marital status ● No of dependants ● How much borrowed? ● Account balance at middle of the month ● Credit score etc… Identify features

Good to have all features in the range -1 to +1 ● numeric x-data can be Gaussian normalized (really depends on your data) ● binary x-data is (-1 +1) encoded ● categorical x-data is 1-of-(C-1) effects-coded ( ex: [0,1] or [1,0] or [-1,-1] ) Prepare features for Neural Network

● numeric y-data is left alone ● binary y-data is 1-of-C dummy-coded ● categorical y-data is 1-of-C dummy-coded

Design Neural Network Model

● Input layer – only one layer. Neurons determined by number of features to be used ● Output layer – only one layer. Usually one neuron except for multiclass classification (run away: 0, return money: 1) Introducing “maybe” = 3 neurons (run away: {1,0,0}, maybe: {0,1,0}, return money:{0,0,1}

● Hidden layers – linearly separable data requires no hidden layer. Rare problems requiring more than two hidden layer. One is usually enough. There are many rule-of-thumb methods: ● The number of hidden neurons should be between the size of the input layer and the size of the output layer. ● The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. ● The number of hidden neurons should be less than twice the size of the input layer.

● Too few hidden neurons result in under-fitting (model is not able to get good results) ● Too many hidden neurons results in over-fitting (model fits well with training data but does not perform well with test data)

Always better to use existing library/services ● Azure machine learning services ● Weka ● C# - Aforge.Net, NeuronDotNet ● Java – Neuroph ● Python – PyBrain, NeuroLab ● Php - Neural Mesh ● R - nnet Coding the Neural Network

● Epoch – number of iterations (too much can result to model over-fitting) ● Learning rate – how much an updating step influences the current value of the weights (too small – runs for long; too large – can go past optimum weights) ● Momentum – used to diminish the fluctuations in weight changes over consecutive iterations Network Configuration Terms

Training using Aforge.Net

Predicting using Aforge.Net

● Analysing the result

Where: ● Vi = Original output of data ● Vo = Output from neural net ● Vl = length of data ● TP = True Positive – accurately predicted valid for a valid transaction. ● FP = False Positive – predicted valid for a fraudulent transaction. ● FN = False Negative – predicted fraudulent for a valid transaction.

Thank you