.. _library_adaptive_boosting_classifier:

adaptive_boosting_classifier

Adaptive Boosting (aka AdaBoost) classifier using C4.5 decision trees as base learners. Implements the SAMME (Stagewise Additive Modeling using a Multi-class Exponential loss function) variant, which supports multi-class classification by adjusting the weight update formula to account for the number of classes. Builds an ensemble of weighted decision trees where each subsequent tree focuses on the examples misclassified by previous trees: after each iteration, the weights of misclassified examples are increased so that subsequent learners focus more on difficult cases.

The library implements the classifier_protocol defined in the classification_protocols library. It provides predicates for learning an ensemble classifier from a dataset, using it to make predictions (with class probabilities), and exporting it as a list of predicate clauses or to a file.

Datasets are represented as objects implementing the dataset_protocol protocol from the classification_protocols library. See test_files directory for examples.

API documentation

Open the `../../docs/library_index.html#adaptive_boosting_classifier <../../docs/library_index.html#adaptive_boosting_classifier>`__ link in a web browser.

Loading

To load all entities in this library, load the loader.lgt file:

::

| ?- logtalk_load(adaptive_boosting_classifier(loader)).

Testing

To test this library predicates, load the tester.lgt file:

::

| ?- logtalk_load(adaptive_boosting_classifier(tester)).

Features

• Adaptive Boosting: Iteratively trains weighted decision trees, focusing on misclassified examples
• SAMME Algorithm: Supports multi-class classification via the SAMME variant of AdaBoost
• C4.5 Base Learners: Uses C4.5 decision trees as weak learners for each boosting round
• Weighted Voting: Final predictions determined by weighted voting where more accurate learners have higher weight
• Probability Estimation: Provides confidence scores based on weighted vote proportions
• Early Stopping: Training stops early if a perfect classifier is found or if a base learner is worse than random
• Configurable Options: Number of estimators (boosting rounds) configurable via predicate options
• Classifier Export: Learned classifiers can be exported as predicate clauses

Options

The following options can be passed to the learn/3 predicate:

• number_of_estimators(N): Number of boosting rounds / weak learners (default: 10)

Classifier representation

The learned classifier is represented as a compound term:

::

ab_classifier(WeightedTrees, ClassValues, Options)

Where:

• WeightedTrees: List of weighted_tree(Alpha, C45Tree, AttributeNames) elements
• ClassValues: List of possible class values
• Options: List of options used during learning When exported using export_to_clauses/4 or export_to_file/4, this classifier term is serialized directly as the single argument of the generated predicate clause so that the exported model can be loaded and reused as-is.

References

1. Freund, Y. and Schapire, R.E. (1997). "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting". Journal of Computer and System Sciences, 55(1), 119-139.
2. Zhu, J., Zou, H., Rosset, S., and Hastie, T. (2009). "Multi-class AdaBoost". Statistics and Its Interface, 2(3), 349-360.
3. Quinlan, J.R. (1993). "C4.5: Programs for Machine Learning". Morgan Kaufmann.

Usage

Learning a Classifier ~~~~~~~~~~~~~~~~~~~~~

::

% Learn an AdaBoost classifier with default options (10 estimators) | ?- adaptive_boosting_classifier::learn(play_tennis, Classifier). ...

% Learn with custom options | ?- adaptive_boosting_classifier::learn(play_tennis, Classifier, [number_of_estimators(20)]). ...

Making Predictions ~~~~~~~~~~~~~~~~~~

::

% Predict class for a new instance | ?- adaptive_boosting_classifier::learn(play_tennis, Classifier), adaptive_boosting_classifier::predict(Classifier, [outlook-sunny, temperature-hot, humidity-high, wind-weak], Class). Class = no ...

% Get probability distribution from weighted voting | ?- adaptive_boosting_classifier::learn(play_tennis, Classifier), adaptive_boosting_classifier::predict_probabilities(Classifier, [outlook-overcast, temperature-mild, humidity-normal, wind-weak], Probabilities). Probabilities = [yes-0.9, no-0.1] ...

Exporting the Classifier

::

   % Export as predicate clauses
   | ?- adaptive_boosting_classifier::learn(play_tennis, Classifier),
        adaptive_boosting_classifier::export_to_clauses(play_tennis, Classifier, my_boost, Clauses).
   ...

   % Export to a file
   | ?- adaptive_boosting_classifier::learn(play_tennis, Classifier),
        adaptive_boosting_classifier::export_to_file(play_tennis, Classifier, my_boost, 'boost.pl').
   ...

Using a Saved Classifier

::

% Load and use a previously saved classifier | ?- logtalk_load('boost.pl'), my_boost(Classifier), adaptive_boosting_classifier::predict(Classifier, [outlook-sunny, temperature-cool, humidity-normal, wind-weak], Class). Class = yes ...

Printing the Classifier ~~~~~~~~~~~~~~~~~~~~~~~

::

% Print a summary of the AdaBoost classifier | ?- adaptive_boosting_classifier::learn(play_tennis, Classifier), adaptive_boosting_classifier::print_classifier(Classifier).

AdaBoost Classifier ===================

Learning options: [number_of_estimators(10)]

Class values: [yes,no] Number of estimators: 10

Weighted trees: Estimator 1 (alpha=1.2345, features: [outlook,temperature,humidity,wind]): -> tree rooted at outlook ... ...