The function builds a deep isolation forest that uses fuzzy logic to determine if a record is anomalous or not.
The function takes a wide-format <code>data.frame</code> object as input and returns it with two appended vectors.
The first vector contains the anomaly scores as numbers between zero and one, and the second vector provides
a set of logical values indicating whether the records are outliers (<code>TRUE</code>) or not (<code>FALSE</code>).

distribution

probability

outliers

The presence of outliers in a dataset can substantially bias the
results of statistical analyses. To correct for outliers, micro edits are
manually performed on all records. A set of constraints and decision rules
is typically used to aid the editing process. However, straightforward
decision rules might overlook anomalies arising from disruption of linear
relationships. Computationally efficient methods are provided to
identify historical, tail, and relational anomalies at the data-entry
level (Sartore et al., 2024; <doi:10.6339/24-JDS1136>). A score statistic
is developed for each anomaly type, using a distribution-free approach
motivated by the Bienaymé-Chebyshev's inequality, and fuzzy logic is used
to detect cellwise outliers resulting from different types of anomalies.
Each data entry is individually scored and individual scores are combined
into a final score to determine anomalous entries. In contrast to fuzzy
logic, Bayesian bootstrap and a Bayesian test based on empirical
likelihoods are also provided as studied by Sartore et
al. (2024; <doi:10.3390/stats7040073>). These algorithms allow for a more
nuanced approach to outlier detection, as it can identify outliers at
data-entry level which are not obviously distinct from the rest of the
data.
---
This research was supported in part by the U.S. Department of Agriculture,
National Agriculture Statistics Service. The findings and conclusions in
this publication are those of the authors and should not be construed to
represent any official USDA, or US Government determination or policy.

Luca Sartore

HRTnomaly

Historical, Relational, and Tail Anomaly-Detection Algorithms

Lu Chen

Justin van Wart

Andrew Dau

Valbona Bejleri

dif function

<dl><dt>dta</dt>
<dd>A wide-format <code>data.frame</code> object with records (stored by row).</dd>
<dt>nt</dt>
<dd>Number of deep isolation trees to build to form the forest. By default, it is set to <code>100</code>.</dd>
<dt>nss</dt>
<dd>Number of subsamples used to build a single deep isolation tree.
If set (by default) to <code>NULL</code>, the program will randomly select 25% of the records provided to the <code>dta</code> argument.</dd>
<dt>threshold</dt>
<dd>A number between zero and one used as a threshold when identifying outliers from the anomaly scores.
By default, this argument is set to <code>0.95</code>, so that 5% of the records is going to be classified as anomalous.</dd></dl>

Arguments

Luca Sartore <a href="/link/drwolf85%40gmail.com?package=HRTnomaly&version=25.11.22" data-mini-rdoc="HRTnomaly::drwolf85@gmail.com">drwolf85@gmail.com</a>

Author

Deep Isolation Forest — dif

<dl>

<dt>dta</dt>
<dd>A wide-format <code>data.frame</code> object with records (stored by row).</dd>


<dt>nt</dt>
<dd>Number of deep isolation trees to build to form the forest. By default, it is set to <code>100</code>.</dd>


<dt>nss</dt>
<dd>Number of subsamples used to build a single deep isolation tree.
If set (by default) to <code>NULL</code>, the program will randomly select 25% of the records provided to the <code>dta</code> argument.</dd>


<dt>threshold</dt>
<dd>A number between zero and one used as a threshold when identifying outliers from the anomaly scores.
By default, this argument is set to <code>0.95</code>, so that 5% of the records is going to be classified as anomalous.</dd>

</dl>

Luca Sartore <a href='mailto:drwolf85@gmail.com'>drwolf85@gmail.com</a>

dif: Deep Isolation Forest

Description

Usage

Value

Arguments

Author

Details

Examples