neural network for classification

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DATA MINING

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WHY DATA MINING?

Data explosion problem Automated data collection tools and mature database

technology lead to tremendous amounts of data stored in

databases, data warehouses and other information

repositories We are drowning in data, but starving for knowledge!

Solution: Data mining

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DATA MINING

Data mining (knowledge discovery in databases)[3]:

Extraction of interesting (non-trivial, implicit,

previously unknown and potentially useful)

information or patterns from data in large databases

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DATA MINING TASKS

Data Mining includes the following tasks :

Classification: Classifies a data item into one of several predefinedcategories.

Regression: Maps a data item to a real-valued prediction variable.

Clustering: Maps a data item into a cluster, where clusters are natural

groupings of data items based on similarity metrics.Association rules: Describes association relationship among different

attributes.

Summarization: Provides a compact description for a subset of data.

Dependency modeling: Describes significant dependencies among

variables.Sequence analysis: Models sequential patterns, like time-series analysis.The goal is to model the state of the process generating the sequenceor to extract and report deviations and trends over time.

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DATA MINING CLASSIFICATION TECHNIQUES

Classification Techniques:-

Decision Tree based Methods

Rule-based Methods

Memory based reasoning

Genetic Algorithms

Bayesian Belief Networks

Support Vector Machines

Neural Networks

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INTRODUCTION TO NEURAL

NETWORKS

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WHAT IS NEURAL NETWORK?

Biologically motivated approach to machine learning.

A neural network is a powerful data modeling tool thatis able to capture and represent complex input/outputrelationships.

Neural networks resemble the human brain in thefollowing two ways:

* A neural network acquires knowledge through

learning.* A neural network's knowledge is stored within inter-neuron connection strengths known as synapticweights.

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BIOLOGICAL NEURON

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ARTIFICIAL NEURON

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ANN ARCHITECTURE

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DATA MINING CLASSIFICATION USING

ARTIFICIAL NEURAL NETWORKS

The Data Mining Classification using Artificial Neural Networkshas eight steps:

Step 1: (Data collection) The data to be used for classification is

collected. Step 2: (Training and testing data separation) The available data

are divided into training and testing data sets of size 80% and20 % respectively.

Step 3: (Network architecture) A network architecture and alearning method are selected. Important considerations are theexact number of perceptrons and the number of layers.

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Step 4: (Parameter tuning and weight initialization)There areparameters for tuning the network to the desired learningperformance level. Part of this step is initialization of thenetwork weights and parameters, followed by modification of

the parameters as training performance feedback is received.Initialize weight and biases to the random numbers distributed

over a small range of values:

[-/sqrt(Ni ) , +/sqrt(Ni )]

Where Ni -No. of inputs to ith unit, - integer between 1 to 3

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Step 5: (Data Normalization) Transforms theapplication data into the type and format required bythe ANN.

All data must be normalized i.e. all values of theattributes in the database are changed to contain in

the interval [0,1] or [-1,1].

Two normalization techniques are used:

1. Max-Min Normalization

2. Decimal scaling Normalization

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DATA NORMALIZATION

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DATA NORMALIZATION

Decimal Scaling Normalization:

Normalization by decimal scaling normalizes by

moving the decimal point of values of attribute A.

v=v/10j

Where j is smallest integer such that max|v|


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Step 6: (Training) Training is conducted iteratively bypresenting input and desired or output data to the ANN. The

ANN computes the outputs and adjusts the weights until thecomputed outputs are within an acceptable tolerance of the

known outputs for the input cases.

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Step 7: (Testing) The testing examines the performance ofthe network using the derived weights by measuring the

ability of the network to classify the testing data correctly.

Step 8: (Implementation) Now a stable set of weights areobtained.

Now the network can reproduce the desired output for the

given inputs like those in the training set.

The network is ready to use as a stand-alone system or aspart of another software system where new input data will

be presented to it and its output will be a recommended

decision.

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NEURAL NETWORK CLASSIFICATION

USING BACKPROPAGATION ALGORITHM

1. Initialize weight and biases.

2. Feed the training sample.

3. Propagate the inputs forward; we compute the net

input and output of each unit in the hidden and

output layers.

4. Back propagate the error.

5. Update weight and biases to reflect the propagatederrors.

6. Terminating conditions.

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BACKPROPAGATION FORMULAS

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Applications, Benefits

& Limitations

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SOME ANN APPLICATIONSANN application areas:

Tax form processing to identify tax fraud Enhancing auditing by finding irregularities

Bankruptcy prediction

Customer credit scoring

Loan approvals Credit card approval and fraud detection

Financial prediction

Energy forecasting

Computer access security (intrusion detection andclassification of attacks)

Fraud detection in mobile telecommunication networks

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BENEFITS AND LIMITATIONS OF NEURAL

NETWORKSBenefits of ANNs:

Usefulness for pattern recognition, classification,generalization, abstraction and interpretation of incompleteand noisy inputs. (e.g. handwriting recognition, imagerecognition, voice and speech recognition, weatherforecasting).

Resemblance with the functioning of human brain

Ability to solve new kinds of problems. ANNs are particularlyeffective at solving problems whose solutions are difficult, ifnot impossible, to define. This opened up a new range ofdecision support applications formerly either difficult orimpossible to computerize.

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Benefits of ANNs Robustness. ANNs tend to be more robust than their

conventional counterparts. They have the ability to cope withincomplete or fuzzy data. ANNs can be very tolerant of faultsif properly implemented.

Fast processing speed. Because they consist of a largenumber of massively interconnected processing units, alloperating in parallel on the same problem, ANNs canpotentially operate at considerable speed (whenimplemented on parallel processors).

Flexibility and ease of maintenance. ANNs are very flexible inadapting their behavior to new and changing environments.They are also easier to maintain, with some having the abilityto learn from experience to improve their own performance.


NETWORKS (contd.)

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Limitations of ANNs:

ANNs lack explanation capabilities.Justifications for results is difficult toobtain because the connection weightsusually do not have obviousinterpretations .


NETWORKS (contd.)

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future scope &

conclusion

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FUTURE SCOPE

Neural Network a fast and parallel processing

network further may use for attribute

selection and dimensionality reduction

problem.

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CONCLUSION

Even if ANN lacks in explanation capabilities

but because of its robustness, fast and

parallel processing and flexible nature Neural

Network is most useful tool for classification.

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REFERENCES

[1] Christopher M.Bishop, Neural Networks for Pattern recognition ,OxfordUniversity Press.

[2] A.Verikas, M.Bacauskiene, Feature selection with neural networks, Patternrecognition Letters (23) (2002) Page No. 1323-1335.

[3] Ernst Haselsteiner and Gert Pfurtscheller, Using Time-Dependent Neural

Networks for EEG Classification, IEEE transactions on rehabilitation engineering,vol. 8, no. 4, December 2000

[4] E. Hosseini Aria, J. Amini, M.R.Saradjian, Back Propagation Neural Network forClassification of IRS-1D Satellite Images ,

[5] Donald F. Specht, A General Regression Neural Network, IEEE transactions onneural networks. Vol. 2 . No. 6. November 1991

[6] Shivajirao M. Jadhav ,Sanjay L. Nalbalwar,Ashok A. Ghatol , Artificial Neural

Network Models based Cardiac Arrhythmia Disease Diagnosis from ECG SignalData, International Journal of Computer Applications, 2012 by IJCA JournalVolume 44 - Number 15 Year of Publication: 2012

[7] Parick K. Simpson, Fuzzy Min- Max Neural Networks: Part I Classification , IEEEtransaction on Neural Networks, Vol 3, No.5 , September 1992

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Thank you !!!

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neural network for classification

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