Genetic programming with boosting for ambiguities in regression problem
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Genetic Programming With Boosting for Ambiguities in Regression Problem. Grégory Paris Laboratoire d’informatique du Littoral Université du Littoral-côte d’Opale 62228 Calais Cedex, France. [email protected] What Are Ambiguities?.

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Genetic programming with boosting for ambiguities in regression problem

Genetic Programming With Boosting for Ambiguities in Regression Problem

Grégory Paris

Laboratoire d’informatique du Littoral

Université du Littoral-côte d’Opale

62228 Calais Cedex, France

[email protected]

What are ambiguities
What Are Ambiguities? Regression Problem

For a given x, several values are possible for f(x).

Contents Regression Problem

  • Boosting to get several values

    • Boosting in few words

    • GPboost: our algorithm for regression problem

    • Boosting deals with ambiguities, clusters the data

  • Dealing with several values: Dendrograms

    • Presentation

    • Application

  • Results and conclusion

Presentation of boosting
Presentation of Boosting Regression Problem

  • Introduced by Freund and Schapire in 90’s

  • Improvement of machine learning methods

  • For weak learners methods (methods that perform better than a random search)

  • Decrease of error on learning set is assured

  • Makes several hypothesis on different distributions

  • Makes them vote to get a final hypothesis

Boosting and gp
Boosting and GP Regression Problem

  • Iba’s version in 1999

  • Distributions are used to build the fitness set

  • Our version in 2001

  • Distribution is included in the fitness function

Gpboost notation

Fitness set: Regression Problem


Each example has a weight

Initial weight is for each example

will be run T times (T rounds of boosting) with different distributions


« Weak Learner » :

: a GP algorithm including distribution in its fitness

Fitness function:

Gpboost main loop

For do Regression Problem

Run using

The best-of-run is denoted

is the confidence given to function

: error on

: Normalization factor

GPboost (main loop)

Update distribution for the next round:

End For

Gpboost final hypothesis

Each function gives a value for x Regression Problem

A median weighted by confidence values is computed

Others medians provide similar results

GPboost(final hypothesis)

Using boosting 1
Using Boosting (1) Regression Problem

  • Principle of boosting is to focus on points which have not been matched on previous round

  • In ambiguities, all the points can not be matched with one function

  • Using weights to alternatively focus on ambiguities.

Using boosting 2

Target Regression Problem


  • e.g.

Using Boosting(2)

  • We are seeking a fitness function which will focus on extrema rather than average points

Application Regression Problem

  • We run GPboost on this ambiguities problem

  • We use our fitness function

  • We set T=6, the number of rounds

Run of boosting
Run of Boosting Regression Problem

Merging the data
Merging the data Regression Problem

  • We are given 6 functions

  • For a given x, we can provide 6 values

  • We have to find a way to pick up 2 values among the 6.

  • We propose dendrograms to solve this problem

Dendrogram Regression Problem

  • T values

  • Cluster the set of values and take the median of each cluster

  • To cluster the values, we build dendrogram

  • Start with T clusters

  • At each step, group the two nearest clusters

Dendrogram building
Dendrogram (Building) Regression Problem

S={-1.1; -1; 0; 0.15; 1; 1.05}

Cut the dendrogram
Cut the dendrogram Regression Problem

  • The dendrogram must be cut off at a height corresponding to the number of values we want.

Computing cut off values
Computing cut-off values Regression Problem

  • A fixed cut-off value gives better results but needs a priori knowledge of the problem

  • Dynamic cut-off value

  • The number of values will be computed in order to reduce the error made on fitness set on each ambiguity

Computing cut-off Value Regression Problem

Results Regression Problem

With dynamic cut-off value

With static cut-off value

Other benchmarks
Other Benchmarks Regression Problem

  • Inverting

Other benchmarks1
Other Benchmarks Regression Problem

  • Inverting

Conclusion and future work
Conclusion and Future Work Regression Problem

  • Good results on classical and simple problems

    To do

  • Improving cut-off value

  • Applying to real problems