The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Evaluation of feature selection methods for text classification with small datasets using multiple criteria decision-making methods
Open accessAbstract
The evaluation of feature selection methods for text classification with small sample datasets must consider classification performance, stability, and efficiency. It is, thus, a multiple criteria decision-making (MCDM) problem. Yet there has been few research in feature selection evaluation using MCDM methods which considering multiple criteria. Therefore, we use MCDM-based methods for evaluating feature selection methods for text classification with small sample datasets. An experimental study is designed to compare five MCDM methods to validate the proposed approach with 10 feature selection methods, nine evaluation measures for binary classification, seven evaluation measures for multi-class classification, and three classifiers with 10 small datasets. Based on the ranked results of the five MCDM methods, we make recommendations concerning feature selection methods. The results demonstrate the effectiveness of the used MCDM-based method in evaluating feature selection methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Machine learning in automated text categorization
Sebastiani, F. (2002) · ACM Computing Surveys · doi:10.1145/505282.505283 · Cited by 7,926
A systematic analysis of performance measures for classification tasks
Sokolova, M., & Lapalme, G. (2009) · Information Processing & Management · doi:10.1016/j.ipm.2009.03.002 · Cited by 7,515
The relationship between Precision-Recall and ROC curves
Davis, J., & Goadrich, M. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143874 · Cited by 6,559
A Comparative Study on Feature Selection in Text Categorization
Yang, Y., & Pedersen, J. (1997) · Cited by 4,790
Learning Word Vectors for Sentiment Analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011) · Cited by 3,751
Text classification from labeled and unlabeled documents using EM
Nigam, K., Mccallum, A. K., Thrun, S., & Mitchell, T. (2000) · Machine Learning · doi:10.1023/a:1007692713085 · Cited by 2,736
Rokach, L. (2010) · Artificial Intelligence Review · doi:10.1007/s10462-009-9124-7 · Cited by 2,657
Multiple Objective Decision Making ― Methods and Applications: A State-of-the-Art Survey
Hwang, C.-L., & Yoon, K. (1979) · Medical Entomology and Zoology · Cited by 1,853
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
Joachims, T. (1997) · Cited by 1,262
STATE OF ART SURVEYS OF OVERVIEWS ON MCDM/MADM METHODS
Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014) · Technological and Economic Development of Economy · doi:10.3846/20294913.2014.892037 · Cited by 931
Cited by
Explainable AI for Healthcare 5.0: Opportunities and Challenges
Saraswat, D., Bhattacharya, P., Verma, A., Prasad, V. K., Tanwar, S., Sharma, G., Bokoro, P. N., & Sharma, R. (2022) · IEEE Access · doi:10.1109/access.2022.3197671 · Cited by 523
Review of swarm intelligence-based feature selection methods
Rostami, M., Berahmand, K., Nasiri, E., & Forouzandeh, S. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104210 · Cited by 451
Zhang, H., Zhao, S., Kou, G., Li, C.-C., Dong, Y., & Herrera, F. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.03.001 · Cited by 355
Self-citationTextual Feature Extraction Using Ant Colony Optimization for Hate Speech Classification
Gite, S., Patil, S., Dharrao, D., Yadav, M., Basak, S., Rajendran, A., & Kotecha, K. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010045 · Cited by 37
A Novel Hybrid Fuzzy AHP-GA Method for Test Sheet Question Selection
İnce, M., Yiğit, T., & Hakan Işik, A. (2020) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622020500054 · Cited by 16
Metadata
- Authors
- Kou, G., Yang, P., Peng, Y., Xiao, F., Chen, Y., & Alsaadi, F. E.
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 86
- Pages
- 105836
- ISSN
- 1568-4946 (ISSN-L)
- OpenAlex
- W2981842822
- Cited by
- 357
- References
- 51
- FWCI
- 25.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Evaluation of feature selection methods for text classification with small datasets using multiple criteria decision-making methods. https://app.cochat.ai/papers/10.1016/j.asoc.2019.105836
- The paper
- Kou, G., Yang, P., Peng, Y., Xiao, F., Chen, Y., & Alsaadi, F. E. (2020). Evaluation of feature selection methods for text classification with small datasets using multiple criteria decision-making methods. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2019.105836
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
