classifier_protocols

This library provides protocols used in the implementation of machine learning classifier algorithms. Datasets are represented as objects implementing the dataset_protocol protocol. Classifiers are represented as objects implementing the classifier_protocol protocol.

This library also provides test datasets. See below for details.

Logtalk currently provides several classifiers including c45, knn, naive_bayes, nearest_centroid, and random_forest. See these libraries documentation for details.

API documentation

Open the ../../apis/library_index.html#classifier_protocols link in a web browser.

Loading

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

| ?- logtalk_load(classifier_protocols(loader)).

Test datasets

Several sample datasets are included in the test_files directory:

  • Play Tennis — The classic weather/tennis dataset with 14 examples and 4 discrete attributes (outlook, temperature, humidity, wind). Originally from Quinlan (1986) and widely used in machine learning textbooks including Mitchell (1997). Also available from the UCI Machine Learning Repository: https://archive.ics.uci.edu/dataset/349/tennis+major+tournament+match+statistics

  • Contact Lenses — A dataset with 24 examples and 4 discrete attributes (age, spectacle prescription, astigmatism, tear production rate) for deciding the type of contact lenses to prescribe. Originally from Cendrowska, J. (1987). PRISM: An algorithm for inducing modular rules. International Journal of Man-Machine Studies, 27(4), 349-370. Available from the UCI Machine Learning Repository: https://archive.ics.uci.edu/dataset/58/lenses

  • Iris — The classic Iris flower dataset with 150 examples and 4 continuous attributes (sepal length, sepal width, petal length, petal width) for classifying iris species (setosa, versicolor, virginica). Originally from Fisher, R.A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179-188. Available from the UCI Machine Learning Repository: https://archive.ics.uci.edu/dataset/53/iris

  • Breast Cancer — A dataset with 286 examples and 9 discrete attributes (age, menopause, tumor size, inv-nodes, node-caps, degree of malignancy, breast, breast quadrant, irradiation) for predicting breast cancer recurrence events. Contains missing values (9 examples with missing values in the node-caps and breast-quad attributes, represented using anonymous variables). Originally from the Institute of Oncology, University Medical Centre, Ljubljana, Yugoslavia. Donors: Ming Tan and Jeff Schlimmer. Available from the UCI Machine Learning Repository: https://archive.ics.uci.edu/dataset/14/breast+cancer

  • Gaussian Anomalies — A synthetic 2D anomaly detection dataset with 50 examples and 2 continuous attributes (x, y). Normal points are sampled from a standard normal distribution centered at the origin. Anomalous points are placed far from the cluster center. Inspired by the canonical test case used in the Extended Isolation Forest paper by Hariri et al. (2019).

  • Shuttle Anomalies — A subset of the Statlog Shuttle dataset with 50 examples and 9 continuous attributes representing sensor readings from the NASA Space Shuttle. Class 1 (Rad Flow) is the majority class (normal), while all other classes are treated as anomalies. Originally from Catlett, J. (1991). Available from the UCI Machine Learning Repository: https://archive.ics.uci.edu/dataset/148/statlog+shuttle

  • Water Potability — A water potability dataset with 50 examples and 9 continuous attributes (pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, turbidity). Normal instances represent potable water samples within acceptable ranges. Anomalous instances represent water samples with hazardous contamination levels. Based on the publicly available Water Quality dataset (Kadiwal, A., 2020, Kaggle).

  • Sensor Anomalies — A synthetic industrial sensor anomaly dataset with 40 examples and 3 continuous attributes (temperature, pressure, vibration). Contains missing values (14 examples with missing values, represented using anonymous variables). Normal readings cluster around typical operating ranges. Anomalous readings show extreme values indicating equipment malfunction.