Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification.

Kurnia Muludi, Revita Setianingsih, Ridho Sholehurrohman, Akmal Junaidi

Abstract read

In one paragraph

Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Legendre polynomial transformation and energy-weighted random forests for sequential data classification.Scientific reports · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Kurnia MuludiInformatics and Business Institute Darmajaya, Bandar Lampung, Lampung Province, Indonesia.

Revita SetianingsihComputer Science Department, Faculty of Science, Lampung University, Bandar Lampung, Lampung Province, Indonesia.

Ridho SholehurrohmanComputer Science Department, Faculty of Science, Lampung University, Bandar Lampung, Lampung Province, Indonesia.

Akmal JunaidiComputer Science Department, Faculty of Science, Lampung University, Bandar Lampung, Lampung Province, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The accuracy of most classification methods is significantly affected by missing values. Therefore, this study aimed to propose a data imputation method to handle missing values through the application of nearest neighbor data and fuzzy membership function as well as to compare the results with standard methods. A total of five datasets related to classification problems obtained from the UCI Machine Learning Repository were used. The results showed that the proposed method had higher accuracy than standard imputation methods. Moreover, triangular method performed better than Gaussian fuzzy membership function. This showed that the combination of nearest neighbor data and fuzzy membership function was more effective in handling missing values and improving classification accuracy.

Indexed as

Classification accuracyFuzzy logicFuzzy membership functionMissing valuesNearest neighbor data

Identifiers

PMID38660203

DOI10.7717/peerj-cs.1968

PMCPMC11042039

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.