Elimination of Noisy Samples in Regression Datasets using Noise Filters
Overview
Traditional noise filtering methods aim at removing noisy samples from a classification dataset. This package adapts classic and recent filtering techniques for use in regression problems, and it also incorporates methods specifically designed for regression data. In order to do this, it uses approaches proposed in the specialized literature, such as Martin et al. (2021) [doi:10.1109/ACCESS.2021.3123151] and Arnaiz-Gonzalez et al. (2016) [doi:10.1016/j.eswa.2015.12.046]. Thus, the goal of the implemented noise filters is to eliminate samples with noise in regression datasets.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 25
- Examples that run
- 100%25 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 88%
- rgnoisefilt HTML
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- 0
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Dependencies
Code & Tests
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People & History
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- RR 4.6.0 released · 2026-04-24
- unarchivedReturned to CRAN2025-05-23
- 1.1.3Latest
- archivedRemoved from CRAN2025-05-15requires archived package 'kknn'
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 1.1.22023-10-02 · diff ↗
- 1.1.12023-09-13
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2023-09-13
- Total releases
- 3 / 3 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.2.0
- Download size
- 64 KB
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