Mathematical Programming in Support Vector Machines
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Transcript Mathematical Programming in Support Vector Machines
Support Vector Machine Data Mining
Olvi L. Mangasarian
with
Glenn M. Fung, Jude W. Shavlik
& Collaborators at ExonHit – Paris
Data Mining Institute
University of Wisconsin - Madison
What is a Support Vector Machine?
An optimally defined surface
Linear or nonlinear in the input space
Linear in a higher dimensional feature space
Implicitly defined by a kernel function
K(A,B) C
What are Support Vector Machines
Used For?
Classification
Regression & Data Fitting
Supervised & Unsupervised Learning
Principal Topics
Knowledge-based classification
Incorporate expert knowledge into a classifier
Breast cancer prognosis & chemotherapy
Classify patients on basis of distinct survival curves
Isolate a class of patients that may benefit from
chemotherapy
Multiple Myeloma detection via gene expression
measurements
Drug discovery based on gene macroarray expression
Joint work with ExonHit
Support Vector Machines
Maximize the Margin between Bounding Planes
w
x 0w = í + 1
A+
A-
x 0w = í à 1
2
jj wjj
Principal Topics
Knowledge-based classification (NIPS*2002)
Conventional Data-Based SVM
Knowledge-Based SVM
via Polyhedral Knowledge Sets
Incoporating Knowledge Sets
Into an SVM Classifier
è ?
é
Suppose that the knowledge set: x ? Bx 6 b
belongs to the class A+. Hence it must lie in the
halfspace :
è
é
x j x 0w> í + 1
We therefore have the implication:
Bx 6 b )
x w> í + 1
0
This implication is equivalent to a set of
constraints that can be imposed on the classification
problem.
Numerical Testing
The Promoter Recognition Dataset
Promoter: Short DNA sequence that
precedes a gene sequence.
A promoter consists of 57 consecutive
DNA nucleotides belonging to {A,G,C,T} .
Important to distinguish between
promoters and nonpromoters
This distinction identifies starting locations
of genes in long uncharacterized DNA
sequences.
The Promoter Recognition Dataset
Numerical Representation
Simple “1 of N” mapping scheme for converting
nominal attributes into a real valued representation:
Not most economical representation, but commonly
used.
The Promoter Recognition Dataset
Numerical Representation
Feature space mapped from 57-dimensional nominal
space to a real valued 57 x 4=228 dimensional space.
57 nominal values
57 x 4 =228
binary values
Promoter Recognition Dataset
Prior Knowledge Rules
Prior knowledge consist of the following 64 rules:
2
3
R1
6 or 7
6
7
6 R2 7 V
6
7
6 or 7
6
7
6 R3 7
4
5
or
R4
2
3
R5
6 or 7
6
7
6 R6 7 V
6
7
6 or 7
6
7
6 R7 7
4
5
or
R8
2
3
R9
6 or 7
6
7
6 R10 7
6
7 = ) PROM OTER
6 or 7
6
7
6 R11 7
4
5
or
R12
Promoter Recognition Dataset
Sample Rules
R4 : (pà 36 = T) ^ (pà 35 = T) ^ (pà 34 = G)
^ (pà 33 = A ) ^ (pà 32 = C);
R8 : (pà 12 = T) ^ (pà 11 = A ) ^ (pà 07 = T);
R10 : (pà 45 = A ) ^ (pà 44 = A ) ^ (pà 41 = A );
where pj denotes position of a nucleotide, with
respect to a meaningful reference point starting at
position pà 50 and ending at position p7:
Then:
R4 ^ R8 ^ R10 = )
PROM OTER
The Promoter Recognition Dataset
Comparative Algorithms
KBANN Knowledge-based artificial neural network
[Shavlik et al]
BP: Standard back propagation for neural networks
[Rumelhart et al]
O’Neill’s Method Empirical method suggested by
biologist O’Neill [O’Neill]
NN: Nearest neighbor with k=3 [Cost et al]
ID3: Quinlan’s decision tree builder[Quinlan]
SVM1: Standard 1-norm SVM [Bradley et al]
The Promoter Recognition Dataset
Comparative Test Results
Principal Topics
Breast cancer prognosis & chemotherapy
Kaplan-Meier Curves for Overall Patients:
With & Without Chemotherapy
Breast Cancer Prognosis & Chemotherapy
Good, Intermediate & Poor Patient Groupings
(6 Input Features : 5 Cytological, 1 Histological)
(Clustering: Utilizes 2 Histological Features &Chemotherapy)
253 Patients
(113 NoChemo, 140 Chemo)
Good1:
Lymph=0 AND Tumor<2
Compute Median Using 6 Features
Compute Initial
Cluster Centers
Poor1:
Lymph>=5 OR Tumor>=4
Compute Median Using 6 Features
Cluster 113 NoChemo Patients
Cluster 140 Chemo Patients
Use k-Median Algorithm with Initial Centers:
Use k-Median Algorithm with Initial Centers:
Medians of Good1 & Poor1
Medians of Good1 & Poor1
69 NoChemo Good
Good
44 NoChemo Poor
67 Chemo Good
Intermediate
73 Chemo Poor
Poor
Kaplan-Meier Survival Curves
for Good, Intermediate & Poor Patients
82.7% Classifier Correctness via 3 SVMs
Kaplan-Meier Survival Curves for Intermediate Group
Note Reversed Role of Chemotherapy
Multiple Myeloma Detection
Multiple Myeloma is cancer of the plasma cell
Plasma cells normally produce antibodies
Out of control plasma cells produce tumors
When tumors appear in multiple sites they are called
Multiple Myeloma
Dataset
105 patients: 74 with MM, 31 healthy
Each patient is represented by 7008 gene measurements
taken from plasma cell samples
For each one of the 7008 gene measurements
Absolute Call (AC):
Absent (A), Marginal (M) or Present (P)
Average Difference (AD):
Positive or negative number
Multiple Myeloma Data Representation
A1 0 0
M0 1 0
P 0 0 1
AMP 7008 X 3 = 21024
AD 7008
Total = 28,032 per patient
104 Patients: 74 MM + 31 Healthy
104 X 28,032 Data Matrix A
Multiple Myeloma 1-Norm SVM Linear Classifier
Leave-one-out-correctness (looc) = 100%
Average number of features used = 7 per fold
Total computing time for 105 folds = 7892 sec.
Overall number of features used in 105 folds= 7
Breast Cancer Treatment Response
Joint with ExonHit - Paris (Curie Dataset)
35 patients treated by a drug cocktail
9 partial responders; 26 nonresponders
25 gene expressions out of 692, selected by Arnaud Zeboulon
Most patients had 3 replicate measurements
1-Norm SVM classifier selected 14 out of 25 gene expressions
Leave-one-out correctness was 80%
Greedy combinatorial approach selected 5 genes out of 14
Separating plane obtained in 5-dimensional gene-expression
space
Replicates of all patients except one used in training
Average of replicates of patient left out used for testing
Leave-one-out correctness was 33 out of 35, or 94.2%
Separation of Convex Hull of Replicates of:
10 Synthetic Nonresponders & 4 Synthetic Partial Responders
Linear Classifier in 3-Gene Space
35 Patients with 93 Replicates
26 Nonresponders & 9 Partial Responders
Conclusion
New approaches for SVM-based classification
Algorithms capable of classifying data with few examples in
very large dimensional spaces
Typical of microarray classification problems
Classifiers based on both abstract prior knowledge as well
as conventional datasets
Identification of breast cancer patients that can benefit from
chemotherapy
Useful tool for drug discovery