The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data
Open accessAbstract
The k-Nearest Neighbors classifier is a simple yet effective widely renowned method in data mining. The actual application of this model in the big data domain is not feasible due to time and memory restrictions. Several distributed alternatives based on MapReduce have been proposed to enable this method to handle large-scale data. However, their performance can be further improved with new designs that fit with newly arising technologies.In this work we provide a new solution to perform an exact k-nearest neighbor classification based on Spark. We take advantage of its in-memory operations to classify big amounts of unseen cases against a big training dataset. The map phase computes the k-nearest neighbors in different training data splits. Afterwards, multiple reducers process the definitive neighbors from the list obtained in the map phase. The key point of this proposal lies on the management of the test set, keeping it in memory when possible. Otherwise, it is split into a minimum number of pieces, applying a MapReduce per chunk, using the caching skills of Spark to reuse the previously partitioned training set. In our experiments we study the differences between Hadoop and Spark implementations with datasets up to 11 million instances, showing the scaling-up capabilities of the proposed approach. As a result of this work an open-source Spark package is available.
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UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,156
MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
Nearest neighbor pattern classification
Cover, T., & Hart, P. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1053964 · Cited by 16,936
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 15,539
Witten, I. H. (2008) · Cited by 9,143
Ghemawat, S., Gobioff, H., & Leung, S.-T. (2003) · ACM SIGOPS Operating Systems Review · doi:10.1145/1165389.945450 · Cited by 4,984
Validity of the single processor approach to achieving large scale computing capabilities
Amdahl, G. M. (1967) · Proceedings of the April 18-20, 1967, spring joint computer conference on - AFIPS '67 (Spring) · doi:10.1145/1465482.1465560 · Cited by 4,339
Distance Metric Learning for Large Margin Nearest Neighbor Classification
Weinberger, K. Q., & Saul, L. K. (2009) · Journal of Machine Learning Research · doi:10.5555/1577069.1577078 · Cited by 4,044
Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J. H., McCauley, M., Franklin, M. J. M., Shenker, S. J., & Stoica, I. G. (2012) · Cited by 3,622
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
Philip Chen, C. L., & Zhang, C.-Y. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.015 · Cited by 3,047
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A survey on missing data in machine learning
Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., & Tabona, O. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00516-9 · Cited by 1,067
Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review
Abu Alfeilat, H. A., Hassanat, A. B. A., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., Eyal Salman, H. S., & Prasath, V. B. S. (2019) · Big Data · doi:10.1089/big.2018.0175 · Cited by 667
Ramírez-Gallego, S., Fernández, A., García, S., Chen, M., & Herrera, F. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.10.001 · Cited by 140
Self-citationFog-assisted personalized healthcare-support system for remote patients with diabetes
Devarajan, M., Subramaniyaswamy, V., Vijayakumar, V., & Ravi, L. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01291-5 · Cited by 105
Metadata
- Authors
- Maillo, J., Ramírez, S., Triguero, I., & Herrera, F.
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 117
- Pages
- 3-15
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2432436793
- Cited by
- 316
- References
- 44
- FWCI
- 71.34
- Type
- journal article
- Language
- en
- Open access
- green
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- Maillo, J., Ramírez, S., Triguero, I., & Herrera, F. (2017). kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2016.06.012
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