Sequential and Spatial Supervised Learning

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Sequential and Spatial Supervised Learning
Guohua Hao, Rongkun Shen, ,Dan Vega, Yaroslav
Bulatov and Thomas G. Dietterich School of
Electrical Engineering and Computer Science,
Oregon State University, Corvallis, Oregon 97331
Applications (contd)
Abstract
Methods
Applications (contd)
Traditional supervised learning assumes
independence between the training examples.
However, many statistical learning problems
involve sequential or spatial data that are not
independent. Furthermore, the sequential or
spatial relationships can be exploited to improve
the prediction accuracy of a classifier. We are
developing and testing new practical methods for
machine learning with sequential and spatial
data. This poster gives a snapshot of our
current methods and results.

• Sliding window / Recurrent sliding window

• Experiment result
• Divide the training data into a sub-training set
  and a development (validation) set. Try window
  sizes from 1 to 21 and tree sizes from 10, 20,
  30, 50, 70.
• The best window size was 11, and the best tree
  size was 20. With this configuration, the best
  number of iterations to train was 110, which gave
  66.3 correct predictions on the development set.
  
• Train on the entire training set with this
  configuration and evaluate on the test set. The
  result was 67.1 correct.
• Neural network sliding windows give better
  performance than this, so we are currently
  designing experiments to understand why!

• A classifier is trained, and run on the 8
  rotations and reflections of the test set.
• A majority vote decides the final class.
• A sliding window is used to group the input
  pixels with varying square size, the same is done
  for the output window. Thus, the labeling of a
  pixel is dependent not only on the pixel
  intensity values in its neighborhood, but also on
  the labels placed on the pixels in the
  neighborhood

Protein AA sequence gt1avhb-4-AS IPAYL AETLY
YAMKG AGTDD HTLIR VMVSR SEIDL FNIRK EFRKN FATSL
YSMIK GDTSG DYKKA LLLLC GEDD
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Generate raw profile with PSI-BLAST
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Majority voting
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Classification result
Recurrent Sliding window
Sliding window
Pixel labels by NaiveBayes IC1 OC3 on each of
8 rotations and reflections of the test image

• Hidden Markov Model Joint Distribution P(X,Y)

• Experiment result
• Different window sizes affect not only the
  computation time, but also the accuracy of the
  classifier
• J48 (C4.5) and Naïve Bayes classifiers are the
  most extensively studied. The results show that
  Naïve Bayes achieves a higher accuracy with
  smaller sliding windows, while J48 does better
  with larger window sizes.

Feed into CRF

• Generalization of Naïve Bayesian Networks
• Transition probability P(ytyt-1)
• Observation probability P(xtyt)
• With conditional independence, impractical to
• represent overlapping features of observations

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CRF Training and Testing
Output
Prediction Results
Introduction
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Figure 4

• Semantic Role Labeling

In the classical supervised learning problems, we
assume that the training examples are drawn
independently and identically from some joint
distribution P(x,y). However, many applications
of machine learning involve predicting a sequence
of labels for a sequence of observations. New
learning methods are needed that can capture the
possible interdependencies between labels. We can
formulate this Sequential Supervised Learning
problem as follows Given a set of training
examples of the form (X,Y), where
X(x1,x2,,xn) a sequence of feature vectors
Y(y1,y2,,yn) corresponding label
sequences Goal find a classifier h to predict
new X as Yh(X)
A2 Accepted-from

• The official "shared task" of the 2004 Co-NLL
  conference.
• For each verb in the sentence, find all of its
  arguments and label their semantic roles.

• Conditional Random Field Conditional
  Distribution P(YX)

A1 thing accepted
( IC input context OC output context )
He would nt accept
anything of value from those he was writing
about
V verb

• Extension of logistic regression to sequential
  data
• Label sequence Y forms a Markov random field
• globally conditioned on observation X
• Removes the HMM independence assumption

AM-MOD modal
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A0 acceptor
AM-NEG negation

• When compared to individual pixel classification
  (59) it is easy to see that the recurrent
  sliding window allows for a significant
  improvement in the accuracy of the classifier.
• Currently, the affect bagging and boosting has
  on the accuracy is under investigation.

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• Difficulty for the machine learning
• Humans use background knowledge to figure out
  semantic roles
• There are 70 various semantic role tags, which
  makes it computationally intensive
• Experiment result
• Two forms of feature-inducing in Conditional
  Random Fields
• Regression tree approach
• Incremental field growing
• 70 different semantic tags to learn. Training
  set is 9000, whereas test set is 2,000
• Evaluated using F-measure, the harmonic mean
  between precision and recall of requested
  argument types.
• Both methods got similar performance, F-measure
  around 65. The best published performance was
  71.72, using a simple  greedy left to right
  sequence labeling
• Again, simpler non-relational approaches
  outperform CRF on  this task. Why?

• Potential function of the random field

Conclusions and Future work

• Conditional Probability

• In the recent years, substantial progress has
  already been make to the sequential and spatial
  supervised learning problems. This poster has
  attempted to review some of the existing methods
  and give out our current methods and experiment
  results in several applications. Future work will
  include
• Develop methods that can handle large number of
  classes
• Discriminative methods using large margin
  principles
• Understand why structural learning methods, such
  as CRF, do not outperform classical methods in
  some structural learning problems

• Maximize the log likelihood

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• Vertical relationship as in normal supervised
  learning
• Horizontal relationship interdependencies
  between label variable, can improve accuracy

• Parameter Estimation
• Iterative scaling and gradient descent
  exponential number of parameters
• Gradient tree boosting only necessary
  interactions among features

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• Classification of remotely sensed images

Acknowledgement
Figure 1

• Discriminative methods Score function f(X,Y)

Examples include part of speech tagging, protein
secondary structure prediction etc. Extending
1-D observation and label sequences to 2-D
arrays, we obtain a similar formulation for the
Spatial Supervised Learning problem, where both X
and Y have 2-D structure and interdependencies
between labels.

• Assign the crop identification classes
  (unknown, sugar beets, stubble, bare soil,
  potatoes, carrots) to pixels in the remotely
  sensed image

We thank the National Science Foundation for
supporting this research under grant number
IIS-0307592

• Averaged perceptron ( Michael Collins et al.
  2002 )
• Hidden Markov support vector machine (Yasemin
  Altun et al. 2003 )
• Maximum Margin Markov Network ( Ben Taskar 2003 )

• Training set and test set are created by
  dividing the image in half with a horizontal
  line. The top half is used as the training set,
  and the bottom half as the test set.
• Training Set Expansion rotations and
  reflections of the training set increase the
  training set 8 fold.

Figure 5 Image with true labels of the classes.
Upper part is the training example and the lower
part is the testing example
Reference
Applications

• Dietterich, T.G (2002). Machine learning for
  sequential data a review. Structural,
  Syntactic, and Statistical Pattern Recognition
  (pp. 15-30). New York Springer Verlag
• Lafferty, J., MaCallum, A., Pereira, F. (2001)
  Conditional random fields Probabilistic models
  for segmenting and labeling sequence data.
  Proceedings of the 18th International Conference
  on Machine Learning (pp.282-289). San Francisco,
  CA Morgan Kaufmann
• Dietterich T. G. , Ashenfelter, A., Bulatov,
  Y. (2004) Training Conditional Random Field via
  Gradient Tree Boosting. Proceedings of the 21st
  International Conference on Machine Learning (pp
  217-224) Banff, Canada
• Jones D. T. (1999) Protein Secondary Structure
  Prediction Based Matrices. J. Mol. Biol.
  292195-202
• Cuff J.A. and Barton G.J. (2000) Application of
  Multiple Sequence Alignment Profiles to Improve
  Protein Secondary Structure Prediction. Proteins
  Structure, Function and Genetics 40502-511
• Carreras, X. Marquez, L. Introduction to the
  CoNLL-2004 Shared Task Semantic Role Labeling.
  Proceedings of CoNLL-2004
• Della Pietra, S., Della Pietra, V. Lafferty,
  J. (1997) Inducing features of random fields.
  IEEE Transactions on Pattern Analysis and Machine
  Intelligence, 19(4), 380---393.

yi1,j
yi1,j1

• Protein secondary structure prediction

Structural Supervised Learning Given A graph G
(V,E), each vertex is an (xv,yv) pair.
Some vertexes are missing the y
label Goal Predict the missing y labels

• Assign the secondary structure classes (a-helix,
  b-sheet and coil) to proteins
• amino acid (AA) sequence, leading to tertiary
  and/or quaternary structure
• corresponding to protein functions
• Use Position-Specific Scoring Matrix profiles to
  improve the prediction accuracy
• Use CB513 datasets, with sequences shorter than
  30 AA residues excluded, in
• our experiment

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Figure 2
Figure 6 training set expansion