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  • 7/8/2015 Machine Intelligence Unit, ISI 1

    Soft Computing: Excursion through Fuzzy Sets, Neural

    Networks, Genetic Algorithms,

    and Rough Sets

    SUSHMITA MITRA

    Machine Intelligence Unit

    Indian Statistical Institute

    Email: sushmita@isical.ac.in

  • 7/8/2015 Machine Intelligence Unit, ISI 2

    Contents

     Pattern Recognition

     Soft Computing

     Fuzzy Sets

     Artificial Neural Networks

     Genetic Algorithms

     Rough Sets

     Role in Biomedical Image Analysis

     Radiomics & Radiogenomics

  • 7/8/2015 Machine Intelligence Unit, ISI 3

    Basics of Pattern Recognition

     Classification: Supervised learning

     Clustering: Unsupervised learning

     Feature selection/ Feature extraction

  • 7/8/2015 Machine Intelligence Unit, ISI 4

     Classification:  predicts categorical class labels

     classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data

     Prediction:  models continuous-valued functions, i.e., predicts unknown

    or missing values

     Typical Applications  credit approval

     target marketing

     medical diagnosis

     treatment effectiveness analysis

    Classification vs. Prediction

  • 7/8/2015 Machine Intelligence Unit, ISI 5

    Supervised vs. Unsupervised Learning

     Supervised learning (classification)

     Supervision: The training data (observations,

    measurements, etc.) are accompanied by labels indicating

    the class of the observations

     New data is classified based on the training set

     Unsupervised learning (clustering)

     The class labels of training data is unknown

     Given a set of measurements, observations, etc. with the

    aim of establishing the existence of classes or clusters in

    the data

  • 7/8/2015 Machine Intelligence Unit, ISI 6

    M : Height, Weight, Complexion, Diet….

    …..

    …. ……

    xxxxx xxxxxx

    xxxx B

    P

    F:

    Weight

    Height

    D : Straight Line

    D  Classifier Design

  • 7/8/2015 Machine Intelligence Unit, ISI 7

    Classification methods

     Goal: Predict class Ci = f(x1, x2, .. Xn)

     Regression: (linear or any other polynomial)

     a*x1 + b*x2 + c = Ci.

     Nearest neighour

     Decision tree classifier: divide decision space

    into piecewise constant regions.

     Probabilistic/generative models

     Neural networks: partition by non-linear

    boundaries

  • 7/8/2015 Machine Intelligence Unit, ISI 9

    Pictorially…

    Inter-cluster distances are maximized

    Intra-cluster distances are

    minimized

  • 7/8/2015 Machine Intelligence Unit, ISI 10

    What Is Good Clustering?

     A good clustering method will produce high quality

    clusters with

     high intra-class similarity

     low inter-class similarity

     The quality of a clustering result depends on both

    the similarity measure used by the method and its

    implementation.

     The quality of a clustering method is also measured

    by its ability to discover some or all of the hidden

    patterns.

  • 7/8/2015 Machine Intelligence Unit, ISI 11

    Two different K-means Clusterings

    -2 -1.5 -1 -0.5 0 0.5 1 1.5 2

    0

    0.5

    1

    1.5

    2

    2.5

    3

    x

    y

    -2 -1.5 -1 -0.5 0 0.5 1 1.5 2

    0

    0.5

    1

    1.5

    2

    2.5

    3

    x

    y

    Sub-optimal Clustering

    -2 -1.5 -1 -0.5 0 0.5 1 1.5 2

    0

    0.5

    1

    1.5

    2

    2.5

    3

    x

    y

    Optimal Clustering

    Original Points

  • Soft Computing

  • 7/8/2015 Machine Intelligence Unit, ISI 13

    Soft Computing

     Consortium of methodologies that works

    synergistically and provides flexible information

    processing

     Exploits tolerance for imprecision, uncertainty,

    approximate reasoning and partial truth to

    achieve tractability, low cost solutions and close

    resemblance with human-like decision making

     Provides acceptable solution at low cost (car parking example)

  • 7/8/2015 Machine Intelligence Unit, ISI 14

    Main Components

     Fuzzy Sets

     Artificial Neural Networks

     Genetic Algorithms

     Rough Sets

  • Fuzzy Sets

  • Role of Fuzzy Sets (FS)

     Modeling of imprecise/qualitative knowledge

     Transmission and handling of uncertainties at

    various stages

     Supporting, to an extent, human type

    reasoning in natural form

     Understandability of patterns

     Providing approximate solution faster

  • Precision vs. Imprecision

     Lines 2-3 m long

     Convex areas

     People 7 ft. tall

     Full membership

     Crisp sets: {0,1}

     Crisp models

     Probability

     Fairly short lines

     Almost convex areas

     People about 7 ft. tall

     Partial membership

     Fuzzy sets: [0,1]

     Fuzzy models

     Membership

  • Probabilities vs. Possibilities

    7/8/2015 Machine Intelligence Unit, ISI 18

    When a tiger is about to pounce, only one thing is certain:

    If you take time to compute probabilities, you'll be eaten.

    but if you decide in a split second whether to climb

    (i) the tree to the right or

    (ii) the one ahead,

    Then you have used possibilities

    (as in measures - one uses the min/max operations).

  • 0.0

    0.5

    1.0

     x

    x

    0.0

    0.5

    1.0

     x

    x

    S – membership function

  • 1.0

    0.5

    0.0

     x

    1.0

    0.5

    0.0

     x

    1.0

    0.5

    0.0

      x

     

     - membership function

  • SINGULAR VS. GRANULAR VALUES

    7.3% high

    102.5 very high

    160/80 high

    Singular Granular

    unemployment

    temperature

    blood pressure

  • GRANULATION OF A VARIABLE

     continuous quantized granulated

    Example: Age

    quantized Age 0

    1

    µ 1

    0

    young

    middle

    -aged old

    Age

    µ

    granulated

  • 7/8/2015 Machine Intelligence Unit, ISI 23

    Artificial Neural Networks

  • 7/8/2015 Machine Intelligence Unit, ISI 24

    Background

     Analogous to Biological Nervous System

  • 7/8/2015 Machine Intelligence Unit, ISI 25

    Salient Features

     Biological neuron: Impulse triggered by cell, travels along axon – releases chemical transmitter raising/lowering electric potential – if greater than threshold, causes a pulse

     Neural network: Not programmed, learn by examples; non-parametric

     More efficient than powerful digital computers (number-crunchers) in cognitive tasks

  • 7/8/2015 Machine Intelligence Unit, ISI 26

     Primary task of all biological neural systems is to control various

    functions (mainly behavioral).

     Human being can do it almost instantaneously and without much

    effort. e.g., recognizing a scene or music immediately.

     Artificial Neural Network (ANN) or Neural Network (NN)

    models try to simulate the biological neural network with electronic circuitry.

     Also known as Connectionists Model/ Parallel Distributed

    Processing (PDP).

    Purpose : To achieve human-like performance particularly in

    cognitive tasks like pattern recognition & image processing.

  • 7/8/2015 Machine Intelligence Unit, ISI 27

    Definition : Massively parallel interconnected network of

    simple (usually adaptive) processing elements that interact with

    objects of the real world in a manner similar to biological systems.

     NN models are extreme simplifications of human neural

    systems.

    Computational elements (neurons/nodes/ processors) are

    analogous to that of the fundamental constituents (neurons) of

    the biological nervous system.

    Definition

  • 7/8/2015 Machine Intelligence Unit, ISI 28

    Comparison

     Von Neumann machines: single, fast, powerful CPU; error in 1 bit affects result; memory

     Parallel processing architecture: multiple, complex CPUs; shared memory

     ANN: very simple, slow processing elements; massively parallel interconnections; graceful degradation (fault tolerance); no memory – information encoded redundantly, distributed among connection weights

  • 7/8/2015 Machine Intelligence Unit, ISI 29

    Brief History

     Rosenblatt (’5