Feature Selection on TimeSeries Cab Data

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Feature Selection on Time-Series Cab Data

• Yingkit (Keith) Chow

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Contents

• Introduction
• Features Considered
• FCBF (Filter-type feature selection)
• FCBF-PCA (my variation)
• Conclusion

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All Features Considered

• Features
• Each time sample consists of the following
  features
• Day of Week, Time of Day (1st two features)
• taxist, 69, taxist-1, 69,, taxist-5, 69
• 69 represents the index to the matrix taxis,
  which is the cab entering with meter off, cab
  enter on, cab exit off, cab exit on
• Not all features here will be relevant to
  classifying whether a game is present.

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Fast Correlation-Based Filter

• Algorithm
• Finds features that are relevant ( SU(I, C) gt
  threshold),
• where SU is symmetric uncertainty and will be
  described in the next slide
• Remove redundant features by comparing remaining
  features (after the first step)
• Remove feature j if SU(i, j) gt SU(j, C)

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Equations1

• Information Gain (IG)
• IG(XY) H(X) H(XY)
• Symmetric Uncertainty (SU)
• SU(X,Y) 2 IG(XY) / H(X)H(Y)
• SU is used instead of IG because it compensates
  for features having more values and normalizes
  data1

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FCBF

• Classifier (MATLAB Classify- Linear)
• Number Bins 96
• Threshold 0.01
• Accuracy 91.9

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Choice of Number Bins

• Num Bins 96 results shown in previous slide
  (red is ground truth of game and blue is my
  classification)
• Num Bins 20
• Accuracy 58.6
• Here the algorithm breaks down and only chooses
  feature 2, the time of day. The blue is
  periodic here, where a certain time segment a
  day, everyday will be classed as a game.

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FCBF - PCA

• FCBF compares individual features with each other
• We can use PCA to try and capture a group of
  features. (for example, maybe one eigenvector
  can capture the shape of the number of cabs
  incoming with meters on initially before a game
  or the increase in the number of cabs entering
  with meters off prior to the end of game)
• Example shown in the next slide

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Cab Traffic Behavior

• Before Start of Game
• Cab On Enter and Cab Off Exit are high
• Towards End of Game
• Cab Off Enter and Cab On Exit are high

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FCBF-PCA

• Classifier (MATLAB Classify- Linear)
• Number Bins 20
• Threshold 0.01
• Accuracy 92.9
• Note the features here are projections onto the
  eigenvectors and not the original feature
  dimension

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Conclusions

• The choice of number of bins have an enormous
  impact on the performance. (possibly due to 96
  discrete values of time of day variable)
• FCBF-PCA was less susceptible to the choice of
  numBins (10, 20, 100 numBins all resulted in
  approximately 91 accuracy)

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Future Work

• Currently using labels of game or not game.
• Ill try to make it work for detecting the first
  sample of a game and another classifier to detect
  the last sample of a game since the mid-game
  generally has an entirely different
  characteristic from the beginning and end of
  game. However, I might be limited by the number
  of samples.

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Questions

• Im not currently in NYC so please send questions
  or comments to
• yingkit.chow_at_gmail.com

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Citations

• Feature Selection for High Dimensional Data A
  Fast Correlation-Based Filter Solution, by Lei
  Yu and Huan Liu, ICML (2003)
• Efficient Feature Selection via Analysis of
  Relevance and Redundancy, by Lei Yu and Huan
  Liu, Journal of Machine Learning Research 5
  (2004)