object

isolation_forest_anomaly_detector

Extended Isolation Forest (EIF) algorithm for anomaly detection. Implements the improved version described by Hariri et al. (2019) that uses random hyperplane cuts instead of axis-aligned cuts, eliminating score bias artifacts. Builds an ensemble of isolation trees from baseline training examples selected from a dataset object implementing the anomaly_dataset_protocol protocol. Missing attribute values are represented using anonymous variables.

Availability:

logtalk_load(isolation_forest_anomaly_detector(loader))

Author: Paulo Moura

Version: 2:0:0

Date: 2026-05-06

Compilation flags:

static, context_switching_calls

Imports:

public anomaly_detector_common

Uses:

fast_random(Algorithm)

format

integer

list

numberlist

pairs

type

Remarks:

(none)

Inherited public predicates:

 anomaly_detector_options/2  check_anomaly_detector/1  check_option/1  check_options/1  default_option/1  default_options/1  diagnostic/2  diagnostics/2  export_to_clauses/4  export_to_file/4  learn/2  learn/3  option/2  option/3  predict/3  predict/4  print_anomaly_detector/1  valid_anomaly_detector/1  valid_option/1  valid_options/1

Public predicates

score/3

Computes the anomaly score for a given instance using the learned model. The instance is a list of Attribute-Value pairs where missing values are represented using anonymous variables. The score is in the range [0.0, 1.0]. Scores close to 1.0 indicate anomalies. Scores close to 0.5 or below indicate normal instances.

Compilation flags:

static

Template:

score(Model,Instance,Score)

Mode and number of proofs:

score(+compound,+list,-float) - one

---

score_all/3

Computes the anomaly scores for all instances in the dataset. Returns a list of Id-Class-Score triples sorted by descending anomaly score.

Compilation flags:

static

Template:

score_all(Dataset,Model,Scores)

Mode and number of proofs:

score_all(+object_identifier,+compound,-list) - one

---

Protected predicates

(no local declarations; see entity ancestors if any)

Private predicates

(no local declarations; see entity ancestors if any)

Operators

(none)

See also

anomaly_dataset_protocol, anomaly_detector_protocol, knn_distance_anomaly_detector, lof_anomaly_detector