generalized and heuristic-free feature construction for improved accuracy wei fan ‡, erheng zhong...

Generalized and Heuristic-Free Feature Construction

for Improved Accuracy

Wei Fan‡, Erheng Zhong†, Jing Peng*, Olivier Verscheure‡, Kun Zhang§, Jiangtao Ren†,

Rong Yan‡ and Qiang Yang¶

‡IBM T. J. Watson Research Center†Sun Yat-Sen University

*Montclair State University §Xavier University of Lousiana

Facebook, Inc¶Hong Kong University of Science and Technology

• Construction works when the original pool is not good enough (feature selection won’t work)• Too many choices to construct• Evaluate on local space not always on all the data points• Better Automated

Feature Construction -- Example

3 1 2F F F

XOR like problemNot linearly separable:use both features to construct a “cross” model

Linearly separable:one feature F3 is enough

Main Challenges

To address these, we have 3 main steps

1. Too many ways to construct new features: xy, x-y,x/y, etc• Divide and Conquer

2. Insignificant on the whole data set - highly discriminant in local region • Local Feature Construction and Evaluation

3. Automated – not based on domain knowledge• Automatically adjusted weighting rules

4 binary operators, 1000 original features up to

constructed features64 10

F2 not very usefulunless consideredwith F1

Divide-Conquer

Local Feature Construction and Evaluation

Stopping Criteria: 1.The number of instances in the node is smaller than a threshold2.The node only contains examples from one class

ConstructedFeatures(org + new)

Every node …

(1)

F

(3)

(4)

Weighted

1. Random subset of orig features

2. “Weighted random” subset of operators

(2)

Weighting Rule

Weighting Rule

• Weight is proportional to the info-gain of features constructed by the operator.

Sum of its past info gain

Properties

• Number of features is bounded.

• Highly weighted operator is expected to perform better in its two child nodes (see paper)

• FCTree’s error is bounded.

– also explains why the features are of high quality

Experiment – Data Set

• UCI repository (Balanced)• Caltech-256 database: An image database of 256 obje

ct categories. Each category is processed via a 177-dimensional color correlogram (Balanced)

• Landmine collection: Collected via remote sensing techniques (Skewed)

• Nuclear Ban data source: A nuclear explosion detection problem used by ICDM’08 contest (Skewed)

Experiment -- Baseline methods

• Original Features• TFC:

– enumerates all possible features generated by operators

• NB,SVM and C45• Operators

• FCTree:

Performance--Blannced Data

Best in 23 out of 33 comparisions

Performance--Skew Data

Best in 25 out of 33 comparisions

Scalability Analysis

Strength of Weighting Rule

Original

FCTree

177 dimensioncolor correlogram

Conclusion

• Key points– Divide-conquer to avoid exhaustive enu

meration;– Local feature construction subspace

evaluation– Weighting rules based search: domain

knowledge free and provable performance.

• Code and data available from the authors