Welcome to my presentation on

Regression and Classification: An Artificial Neural Network Approach

Presented byMd. Menhazul Abedin

Research studentDept. of Statistics

University of RajshahiRajshahi-6205

Dedication

• This presentation is dedicated to my honorable supervisor

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Three pioneer of ANN

Warren McCulloch Walter Pitts

Frank Rosenblatt05/02/2023 3

OutlinesMotivation/Why this study?ObjectivesMethodologyFindingsConclusionLimitationArea of further research

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Motivation/Why this study?

• Vector, matrix, sound, image, wave, string, text etc.• How to analyze them? Pitfall of human civilization from several decades.

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Objectives?

• To study neural network as a technique for regression and classification.

• To compare neural network with classical regression and classification techniques.

• To study the limitations of neural network.

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• Structure of neuron

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What is ANN?Biological neural network

Artificial neural network

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• How many hidden layers considered? More hidden layer more approximate nonlinearity • More hidden layer need much time to converge. • Weight adjusted by iterative method (backpropagation)

• Analogy between biological and artificial neural networks

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Historical Background of Artificial Neural Network

• In 1943, neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper on how neurons might work.

• In 1949, Donald Hebb wrote The Organization of Behavior (the ways in which humans learn)

• M. Minsky (1951) built a reinforcement-based network learning system.• F. Rosenblatt (1958) the first practical Artificial Neural Network (ANN) - the

perceptron, • B. Widrow & M.E. Hoff (1960) introduced adaptive percepton-like network using

Least Mean Square (LMS) error algorithm. • 1969 – Marvin Minsky and Seymour showed that perceptron model is not capable

of representing many important problems• 1973 – Christoph Von Der Malsburg used a neuron model that was nonlinear and

biologically more motivated• 1974 – Paul Werbos Developed a learning precedure called backpropagation of

error.

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Historical Background of Artificial Neural Network

• 1986, The application area of the MLP networks remained rather limited until the breakthrough when a general back propagation algorithm for a multi-layered perceptron was introduced by Rummelhart and Mclelland.

• 1988, Radial Basis Function (RBF) networks were first introduced by Broomhead & Lowe. Although the basic idea of RBF was developed 30 years ago under the name method of potential function, the work by Broomhead & Lowe opened a new frontier in the neural network community.

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ANN regression

• Linear activation function Gives continuous values.

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ANN classification

• For two class Sigmoid function ( threshold > 0.5 one class & threshold < 0.5 another class)• More class Softmax function (Gives probability for each class)• tanh function may used as activation function 05/02/2023 13

Activation functions• Linear function ,

• Sigmoid function , Where η=xθ.

• Softmax function,

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Perceptron learning model specifies the probability of a binary output yi ε {0,1} given the input xi as follows:

( | , ) ( | ( , ))i i i ip y x w Ber y sigm x w

1

( | , ) ( | ( , ))n

i ii

p y X w Ber y sigm x w

1

1

1 1( | , ) 11 1

i i

i i

y yn

x w x wi

p y X we e

1; ( 1| , )1 ii i i x wp y x we

Cost function:

1

( ) log ( | , )

= log (1 ) log(1 )n

i i i ii

c w p y X w

y y

Cross entropy

Construction of cost function: sigmoid formulation

sigm(xi,w)=1

1 ix we

Xiw=0

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Softmax formulation

sigm(xi,w)=1

1 ix we+1

xi1

xi2

+1

b1=w10

w11

w21

w12

w22

b2=w20

Ʃ

Ʃ

u11

u12 Softm

ax la

yer

1

1 2 1

i

i i

x w

ix w x w

ee e

2

1 2 2

i

i i

x w

ix w x w

ee e

1 2 1i i 05/02/2023 16

Indicator: 1 if ( )

0 otherwisei

c i

y cI y

0 1( ) ( )1 2( | , ) i iI y I y

i i i ip y x w

0 1( ) ( )1 2

1

( | , ) i i

nI y I yi i

i

p y X w

1

1 2

2

1 2

1

2

y 0( | , )

y 1

i

i i

i

i i

x w

i ix w x w

i i x w

i ix w x w

e ife ep y x we if

e e

0 1 1 21

( ) log ( | , ) ( ( ) log ( ) log )n

i i i ii

c w p y X w I y I y

Construction of cost function: Softmax formulation

XLinear Layer

Log softmax

layerNLL C(w)

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Weight update (Backpropagation)

• Derivative cost w.r.t inputs (layer wise).• Information go from to = c forward message.• Error propagate backward message & update its

weights.

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Optimization

Our goal is to optimize the cost function.Different optimization techniquesGradient descent algorithmNewton's algorithmStochastic gradient descent(SGD)Online learning, batch & mini batch

optimization

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Regression (Findings)• Used data set = 7• (Regression = 4, classification = 3)• Pharmaceuticals data:

Size 26

No. of variables 4 (one dependent and three independent)

Outlier Present (6th , 10th ,and 26th )Autocorrelation AbsenceMulticollinearity AbsenceNormality PresentData type RealCross validation LOOCVApplied methods Linear model, Polynomial & ANN

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Regression (cont…)

ANN is the best regression model05/02/2023 21

Regression(cont..)

• Yacht Hydrodynamics Data:Size 308

No. of variables 7 (one dependent and six independent)

Outlier Absence

Autocorrelation Absence

Multicollinearity Absence

Normality Absence (Clustered)

Data type Real

Cross validation Training set and test set

Applied methods Linear model, Polynomial & ANN

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• Results of Yacht hydrodynamics..

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• 100 times repeat for different training and test set• Box plot of test error grow sense about error variation

• ANN is the best regression model05/02/2023 24

Regression(cont..)• Simulated data-1

Size 1000No. of variables 10 (one dependent and nine independent)Outlier AbsenceAutocorrelation AbsenceMulticollinearity AbsenceNormality presentData type Real Cross validation Training set and test setApplied methods Linear model & ANN

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• Results of Simulated data-1

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• 100 times repeat for different training and test set• Box plot of test error grow sense about error variation

• ANN is the best regression model05/02/2023 27

Regression (cont…)

• Simulated data-2Size 20000No. of variables 20 (one dependent and nine independent)Outlier AbsenceAutocorrelation AbsenceMulticollinearity Strong MulticollinearityNormality presentData type Real Cross validation Training set and test setApplied methods Linear model & ANN

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• Results of Simulated data-2

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• 100 times repeat for different training and test set• Box plot of test error grow sense about error variation

• ANN is the best regression model05/02/2023 30

Classification• IRIS data

Size 150

No. of variables 5 (one dependent and four independent)

No. of class Three (Setosa, Versicolor, Virginica

Type Balanced

Data type Real

Cross validation LOOCV

Applied methods Logistic, LDA, QDA, KNN, NB & ANN

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Classification (cont…)• Results

• ANN is the best classifier

Methods Classification rate Misclassification rate

Logistic 0.98 0.02

LDA 0.98 0.02

QDA 0.98 0.02

KNN 0.95 0.05

NB 0.95 0.05

ANN 0.99 0.01

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Classification (cont…)

• Fertility data

Size 100

No. of variables 5 (one dependent and four independent)

No. of class Two (Normal & Altered)

Type Imbalanced

Data type Real

Cross validation LOOCV

Applied methods Logistic, LDA, KNN, NB & ANN

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Classification (cont…)

• Results

• ANN is the best classifier

Methods Accuracy Sensitivity Specificity PPV NPV

Logistic 0.84 0.87 0.00 0.96 0.00

LDA 0.83 0.95 0.00 0.87 0.00

KNN 0.81 0.90 0.16 0.88 0.20

NB 0.82 0.94 0.00 0.87 0.00

ANN 0.88 0.95 0.34 0.91 0.50

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Classification (cont…)• Leukemia data

Size 72

No. of variables 7130 (one dependent and 7129 independent)

No. of class Two (ALL & AML)

Type Balanced

Data type Real

Cross validation LOOCV

Applied methods Logistic, LDA, QDA, KNN, NB & ANN

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Classification (cont…)• Results

• ANN is the best classifier

Methods Accuracy Sensitivity Specificity

Logistic 0.47 0.62 0.31

LDA 0.62 0.68 0.52

QDA 0.65 1.00 0.00

KNN 0.54 0.65 0.32

NB 0.65 1.00 0.00

ANN 0.64 0.68 0.56

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Conclusion

• In all cases ANN is the best .

Data Problems ANN Status

Pharmaceuticals Outlier Best regression model

Yacht hydro: Clustered Best regression model

Simulated data-1 Fresh Best regression model

simulated data-2 Strong multicollinearity Best regression model

IRIS Balanced Best classifier

Fertility Imbalanced Best classifier

Leukemia Large (7129 varisbles) Best classifier

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Limitations

• Backpropagation no guarantee of absolute minimum • VC dimension unclear• Weights initialization random result is not unique.• Some weights are zero network doesn’t converge.• Computation of confidence interval is so hard.• Doesn’t perform t-test, F-test.

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Areas of further research• Robust, generalized ridge, principle component, latent

root, lasso and step wise regression.• Multivariate regression, time series analysis • Application of artificial neural network on unsupervised

learning• Study of semi supervised learning• Comparative study with others machine learning

techniques and data mining techniques• Improvement of backpropagation algorithm

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THANK YOU ALL

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