machine learning, data mining, genetic algorithms, neural
TRANSCRIPT
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, ,Machine Learning Data Mining , Genetic Algorithms Neural Networks
370ISYS. . Dr R Weber
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•Learner uses:–positive examples (instances ARE examples of a concept) and
–negative examples (instances ARE NOT examples of a concept)
Concept Learning is a Form of Inductive Learning
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•Needs empirical validation•Dense or sparse data determine quality of
different methods
Concept Learning
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•The learned concept should be able to correctly classify new instances of the concept–When it succeeds in a real instance of the concept it
finds true positives – When it fails in a real instance of the concept it
finds false negatives
Validation of Concept Learning i
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•The learned concept should be able to correctly classify new instances of the concept–When it succeeds in a counterexample it finds true
negatives–When it fails in a counterexample it finds false
positives
Validation of Concept Learning ii
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Rule Learning
•Learning widely used in data mining•Version Space Learning is a search
method to learn rules•Decision Trees
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Decision trees•Knowledge representation formalism•Represent mutually exclusive rules (disjunction)•A way of breaking up a data set into classes or
categories•Classification rules that determine, for each
instance with attribute values, whether it belongs to one or another class
•Not incremental
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Decision trees
-leaf nodes (classes)
- decision nodes (tests on attribute values)
-from decision nodes branches grow for each possible outcome of the test
From Cawsey, 1997
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Decision tree induction•Goal is to correctly classify all example data•Several algorithms to induce decision trees:
ID3 (Quinlan 1979) , CLS, ACLS, ASSISTANT, IND, C4.5
•Constructs decision tree from past data•Attempts to find the simplest tree (not
guaranteed because it is based on heuristics)
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•From:– a set of target classes–Training data containing objects of more than one class
•ID3 uses test to refine the training data set into subsets that contain objects of only one class each•Choosing the right test is the key
ID3 algorithm
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• Information gain or ‘minimum entropy’• Maximizing information gain corresponds to minimizing entropy•Predictive features (good indicators of the outcome)
How does ID3 chooses tests
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• Information gain or ‘minimum entropy’• Maximizing information gain corresponds to minimizing entropy•Predictive features (good indicators of the outcome)
Choosing tests
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Bad400salaried1,5004
Very good300Waged3,0003
Very bad600Salaried4,0002
Good200Salaried2,0001
Loan statusRepaymentJob statusMonthy income
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•Link analysis
•Deviation detection
Data mining tasks ii
Rules: • Association generation• Relationships between entities
• How things change over time, trends
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KDD applications• Fraud detection
–Telecom (calling cards, cell phones)–Credit cards–Health insurance
Loan approval Investment analysis Marketing and sales data analysis
Identify potential customers Effectiveness of sales campaign Store layout
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Text mining
The problem starts with a query and the solution is a set of information (e.g., patterns, connections, profiles, trends) contained in several different texts that are potentially relevant to the initial query.
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Text mining applications• IBM Text Navigator
–Cluster documents by content;–Each document is annotated by the 2 most frequently
used words in the cluster;
• Concept Extraction (Los Alamos)–Text analysis of medical records;–Uses a clustering approach based on trigram
representation;–Documents in vectors, cosine for comparison;
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rule-based ES
case-based reasoning
inductive ML, NN
algorithms
deductive reasoning
analogical reasoning
inductive reasoning
search
Problemsolving method
Reasoning type
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Genetic Algorithms( )GA
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Genetic algorithms (i)
• learn by experimentation• based on human genetics, it originates new
solutions • representational restrictions• good to improve quality of other methods e.g.,
search algorithms, CBR• evolutionary algorithms (broader)
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Genetic algorithms (ii)
• requires an evaluation function to guide the process• population of genomes represent possible solutions• operations are applied over these genomes• operations can be mutation, crossover• operations produce new offspring• an evaluation function tests how fit an offspring is • the fittest will survive to mate again
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Genetic Algorithms ii
• http://ai.bpa.arizona.edu/~mramsey/ga.html You can change parameters
• http://www.rennard.org/alife/english/gavgb.html Steven Thompson presented
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Neural Networks( )NN
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~= 2nd-5th week
training vision
the evidence
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the evidence
~= 2nd-5th week
training vision10
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the evidence
~= 2nd-5th week
training vision10
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the evidence
~= 2nd-5th week
training vision
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NN: model of brains
input output
neuronssynapses
electric transmissions:
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Elements
•input nodes•output nodes•links•weights
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terminology• input and output nodes (or units)
connected by links
• each link has a numeric weight
• weights store information
• networks are trained on training s ets (examples) and after are tested on te s t
s e ts to assess networks’ accuracy
• /learning training takes place as weights are updated to reflect the input/output behavior
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The concept
=> mammal
=> bird0 1 1
4 legs flylayeggs
1 0 0
1 Yes, 0 No
=> mammal1 1 0
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The concept
=> mammal
=> bird0 1 1
4 legs flylayeggs
1 0 0
=> mammal1 1 0
1 Yes, 0 No
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The concept
=> mammal
=> bird0 1 1
4 legs flylayeggs
1 0 0
=> mammal1 1 0
0.5 0.5 0.5
1 Yes, 0 No
=> mammal
=> bird0 1 1
4 legs flylayeggs
1 0 0
=> mammal1 1 0
0*0.5+1*0.5+1*0.5= 1
1*0.5+0*0.5+0*0.5= 0.5
1*0.5+1*0.5+0*0.5= 1Goal is to have weights that recognize different representations of mammals and birds as such
0.5 0.5 0.5
=> mammal
=> bird0 1 1
4 legs flylayeggs
1 0 0
=> mammal1 1 0
0*0.5+1*0.5+1*0.5= 1
1*0.5+0*0.5+0*0.5= 0.5
1*0.5+1*0.5+0*0.5= 1Suppose we want bird to be greater 0.5 and mammal to be equal or less than 0.5
0.5 0.5 0.5
=> mammal
=> bird0 1 1
4 legs flylayeggs
1 0 0
=> mammal1 1 0
0*0.25+1*0.25+1*0.5= 0.75
1*0.25+0*0.25+0*0.5= 0.25
1*0.25+1*0.25+0*0.5= 0.5Suppose we want bird to be greater 0.5 and mammal to be equal or less than 0.5
0.25 0.25 0.5
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The trainingOutput=Step(∑w f )
learning takes place as weights are updated to reflect the input/output behavior
=> mammal (1)=> bird (0)
0 1 1
4 legs flies eggs
i=1
i=2
i=3
j=1 j=2 j=3
ij
0 0 0
0 0 0
0 0 0
1 0 0
1 0 0
1 0 0
1 0 0
1 0 0
1 1 1
1 0 0
1 1 1
1 1 1
Goal minimize error between representation of the expected and actual outcome
20
ij
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NN demo… ..
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Characteristics• NN implement inductive learning algorithms (through
generalization) therefore, it requires several training examples to learn
• NN do not provide an explanation why the task performed the way it was
• no explicit knowledge; uses data• Classification (pattern recognition), clustering,
diagnosis, optimization, forecasting (prediction), modeling, reconstruction, routing
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Where are NN applicable?• Where they can form a model from training data
alone;• When there may be an algorithm, but it is not
known, or has too many variables;• There are enough examples available• It is easier to let the network learn from examples• Other inductive learning methods may not be as
accurate
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erApplications (i)
•predict movement of stocks, currencies, etc., from previous data;
•to recognize signatures made (e.g. in a bank) with those stored;
•to monitor the state of aircraft engines (by monitoring vibration levels and sound, early warning of engine problems can be given; British Rail have been testing an application to monitor diesel engines;
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erApplications (ii)
•Pronunciation (rules with many exceptions)•Handwritten character recognition
(network w/ 200,000 is impossible to train, final 9,760 weights, used 7300 examples to train and 2,000 to test, 99% accuracy)
•Learn brain patterns to control and activate limbs as in the “Rats control a robot by thought alone” article
•Credit assignment
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erCMU Driving ALVINN
•learns from human drivers how to steer a vehicle along a single lane on a highway
•ALVINN is implemented in two vehicles equipped with computer-controlled steering, acceleration, and braking
•cars can reach 70 m/h with ALVINN•programs that consider all the problem
environment reach 4 m/h only
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Why using NN for the driving task?
• there is no good theory of driving, but it is easy to collect training samples
• training data is obtained with a human* driving the vehicle–5min training, 10 min algorithm runs
• driving is continuous and noisy• almost all features contribute with useful information
*humans are not very good generators of training instances when they behave too regularly without making mistakes
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• INPUT:video camera generates array of 30x32 grid of input nodes
•OUTPUT: 30 nodes layer corresponding to steering direction
•vehicle steers to the direction of the layer with highest activation
the neural network
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Resourceshttp://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html#what
http://www.ri.cmu.edu/projects/project_160.html
http://www.txtwriter.com/Onscience/Articles/ratrobot.html