{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T20:13:00Z","timestamp":1783368780287,"version":"3.54.6"},"reference-count":34,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2017,3,14]],"date-time":"2017-03-14T00:00:00Z","timestamp":1489449600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2017,7]]},"abstract":"<jats:p>The k-nearest-neighbor classifier is a vital algorithm. In practice, the choice of k is decided by the cross-validation method. We propose a new method for neighborhood size selection based on the data set profile. The distribution of a data set and its intrinsic characteristics are the fundamental factors to the choice of k. A local complexity was computed for each example and a complexity profile was constructed by sorting these local complexity values which try to capture inner structure of a data set. After this, a feature vector was built by combing the local complexity profile and some statistic features of a data set. In addition, a history meta-data set was constructed by using the feature vector as attributes and the optimum k value of data set as the label, which was calculated by using ten cross-validation methods. A predict model was trained based on the historic meta-data set and used to predict optimum k value for a new data set. Some exclusive experiments are conducted to verify the proposed method. The results shows that the local complexity features could reflect the inner structure of a data set which could help find the optimum k for k-NN for different domains.<\/jats:p>","DOI":"10.3233\/jifs-161062","type":"journal-article","created":{"date-parts":[[2017,3,17]],"date-time":"2017-03-17T10:45:10Z","timestamp":1489747510000},"page":"55-65","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["A case based method to predict optimal k value for k-NN algorithm"],"prefix":"10.1177","volume":"33","author":[{"given":"Yang","family":"Zhongguo","sequence":"first","affiliation":[{"name":"Key Lab of Petroleum Data Mining, China University of Petroleum, Beijing, China"},{"name":"Department of Computer, China University of Petroleum, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Hongqi","sequence":"additional","affiliation":[{"name":"Key Lab of Petroleum Data Mining, China University of Petroleum, Beijing, China"},{"name":"Department of Computer, China University of Petroleum, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhu","family":"Liping","sequence":"additional","affiliation":[{"name":"Key Lab of Petroleum Data Mining, China University of Petroleum, Beijing, China"},{"name":"Department of Computer, China University of Petroleum, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Qiang","sequence":"additional","affiliation":[{"name":"Key Lab of Petroleum Data Mining, China University of Petroleum, Beijing, China"},{"name":"Department of Computer, China University of Petroleum, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sikandar","family":"Ali","sequence":"additional","affiliation":[{"name":"Key Lab of Petroleum Data Mining, China University of Petroleum, Beijing, China"},{"name":"Department of Computer, China University of Petroleum, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2017,3,14]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1053964"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-007-0114-2"},{"key":"e_1_3_1_4_2","volume-title":"European Conference on Machine Learning","author":"Latourrette M.","year":"2000","unstructured":"LatourretteM., Toward an explanatory similarity measure for nearest-neighbor classification, European Conference on Machine Learning. SpringerBerlin Heidelberg, 2000."},{"issue":"3","key":"e_1_3_1_5_2","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1214\/aoms\/1177700079","article-title":"A nonparametric estimate of a multivariate density function","volume":"36","author":"Loftsgaarden D.O.","year":"1965","unstructured":"LoftsgaardenD.O. and QuesenberryC.P., A nonparametric estimate of a multivariate density function, The Annals of Mathematical Statistics36(3) (1965), 1049\u20131051.","journal-title":"The Annals of Mathematical Statistics"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1968.10490530"},{"issue":"1","key":"e_1_3_1_7_2","first-page":"127","article-title":"Cross-validation: A review 2","volume":"9","author":"Stone M.","year":"1978","unstructured":"StoneM., Cross-validation: A review 2, Statistics: A Journal of Theoretical and Applied Statistics9(1) (1978), 127\u2013139.","journal-title":"Statistics: A Journal of Theoretical and Applied Statistics"},{"issue":"11","key":"e_1_3_1_8_2","doi-asserted-by":"crossref","first-page":"3507","DOI":"10.1016\/j.patcog.2008.04.009","article-title":"Locally linear reconstruction for instance-based learning","volume":"41","author":"Kang P.","year":"2008","unstructured":"KangP. and ChoS., Locally linear reconstruction for instance-based learning, Pattern Recognition41(11) (2008), 3507\u20133518.","journal-title":"Pattern Recognition"},{"key":"e_1_3_1_9_2","first-page":"341","author":"Meesad P.","year":"2008","unstructured":"MeesadP. and HengpraprohmK., Combination of knn-based feature selection and knn based missing-value imputation of microarray data, In 3rd International Conference on Innovative Computing Information and Control, IEEE ICICIC\u201908, Dalian, China, 2008, 341\u2013344.","journal-title":"3rd International Conference on Innovative Computing Information and Control, IEEE ICICIC\u201908"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1029\/95WR02966"},{"key":"e_1_3_1_11_2","first-page":"52","volume-title":"Ninth International Conference on Grid and Cloud Computing, IEEE","author":"Liu H.","year":"2010","unstructured":"LiuH., et al., A new classification algorithm using mutual nearest neighbors, In Ninth International Conference on Grid and Cloud Computing, IEEE, Nanjing, China, 2010, 52\u201357."},{"issue":"11","key":"e_1_3_1_12_2","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1016\/j.csda.2005.06.007","article-title":"On optimum choice of k in nearest neighbor classification","volume":"50","author":"Ghosh A.K.","year":"2006","unstructured":"GhoshA.K., On optimum choice of k in nearest neighbor classification, Computational Statistics & Data Analysis50(11) (2006), 3113\u20133123.","journal-title":"Computational Statistics & Data Analysis"},{"issue":"5","key":"e_1_3_1_13_2","doi-asserted-by":"crossref","first-page":"2135","DOI":"10.1214\/07-AOS537","article-title":"Choice of neighbor order in nearest-neighbor classification","volume":"36","author":"Hall P.","year":"2008","unstructured":"HallP., ParkB.U. and SamworthR.J., Choice of neighbor order in nearest-neighbor classification, The Annals of Statistics36(5) (2008), 2135\u20132152.","journal-title":"The Annals of Statistics"},{"issue":"9","key":"e_1_3_1_14_2","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1016\/S0167-8655(02)00394-X","article-title":"Choosing k for two-class nearest neighbour classifiers with unbalanced classes","volume":"24","author":"Hand D.J.","year":"2003","unstructured":"HandD.J. and VinciottiV., Choosing k for two-class nearest neighbour classifiers with unbalanced classes, Pattern Recognition Letters24(9) (2003), 1555\u20131562.","journal-title":"Pattern Recognition Letters"},{"issue":"3","key":"e_1_3_1_15_2","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.patcog.2005.08.009","article-title":"Neighborhood size selection in the k-nearest-neighbor rule using statistical confidence","volume":"39","author":"Wang J.","year":"2006","unstructured":"WangJ., NeskovicP. and CooperL.N., Neighborhood size selection in the k-nearest-neighbor rule using statistical confidence, Pattern Recognition39(3) (2006), 417\u2013423.","journal-title":"Pattern Recognition"},{"key":"e_1_3_1_16_2","first-page":"445","article-title":"Comparing connectionist and symbolic learning methods","author":"Quinlan J.R.","year":"1994","unstructured":"QuinlanJ.R., Comparing connectionist and symbolic learning methods, Computational Learning Theory and Natural Learning Systems: Constraints and Prospects, 1994, 445\u2013456.","journal-title":"Computational Learning Theory and Natural Learning Systems: Constraints and Prospects"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2004.12.002"},{"issue":"2","key":"e_1_3_1_18_2","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1111\/1467-9868.00338","article-title":"A probabilistic nearest neighbour method for statistical pattern recognition","volume":"64","author":"Holmes C.C.","year":"2002","unstructured":"HolmesC.C. and AdamsN.M., A probabilistic nearest neighbour method for statistical pattern recognition, Journal of the Royal Statistical Society: Series B (Statistical Methodology)64(2) (2002), 295\u2013306.","journal-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)"},{"key":"e_1_3_1_19_2","first-page":"184","article-title":"Locally adaptive nearest neighbor algorithms","author":"Wettschereck D.","year":"1994","unstructured":"WettschereckD. and DietterichT.G., Locally adaptive nearest neighbor algorithms, Advances in Neural Information Processing Systems (1994), 184\u2013184.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_20_2","volume-title":"European Conference on Principles of Data Mining and Knowledge Discovery","author":"Song Y.","year":"2007","unstructured":"SongY., et al., Iknn: Informative k-nearest neighbor pattern classification, European Conference on Principles of Data Mining and Knowledge Discovery, SpringerBerlin Heidelberg, 2007."},{"issue":"2","key":"e_1_3_1_21_2","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1198\/106186007X208380","article-title":"On nearest neighbor classification using adaptive choice of k","volume":"16","author":"Ghosh A.K.","year":"2007","unstructured":"GhoshA.K., On nearest neighbor classification using adaptive choice of k, Journal of Computational and Graphical Statistics16(2) (2007), 482\u2013502.","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-012-0280-z"},{"key":"e_1_3_1_23_2","first-page":"1","volume-title":"21st Signal Processing and Communications Applications Conference (SIU)","author":"Ozger Z.B.","year":"2013","unstructured":"OzgerZ.B. and AmasyaliM.F., KNN parameter selection via meta learning, In 21st Signal Processing and Communications Applications Conference (SIU)North Cyprus, Turkey (2013), 1\u20134."},{"key":"e_1_3_1_24_2","first-page":"1","article-title":"Locally adaptive k parameter selection for nearest neighbor classifier: One nearest cluster","author":"Bulut F.","year":"2015","unstructured":"BulutF. and AmasyaliM.F., Locally adaptive k parameter selection for nearest neighbor classifier: One nearest cluster, Pattern Analysis and Applications (2015), 1\u201311.","journal-title":"Pattern Analysis and Applications"},{"issue":"1","key":"e_1_3_1_25_2","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.patrec.2016.05.007","article-title":"Natural neighbor: A self-adaptive neighborhood method without parameter K","volume":"80","author":"Zhu Q.","year":"2016","unstructured":"ZhuQ., FengJ. and HuangJ., Natural neighbor: A self-adaptive neighborhood method without parameter K, Pattern Recognition Letters80(1) (2016), 30\u201336.","journal-title":"Pattern Recognition Letters"},{"key":"e_1_3_1_26_2","first-page":"1478","volume-title":"22nd International Conference on Pattern Recognition","author":"Bhattacharya G.","year":"2014","unstructured":"BhattacharyaG., GhoshK. and ChowdhuryA.S., Test Point Specific k Estimation for kNN Classifier, In 22nd International Conference on Pattern Recognition, Stockholm, Sweden, ICPR 2014, IEEE, Stockholm, Sweden, 2014, 1478\u20131483."},{"key":"e_1_3_1_27_2","unstructured":"HassanatA.B. et al. Solving the problem of the K parameter in the kNN classifier using an ensemble learning approach. arXiv preprint arXiv: 1409.0919 2014."},{"key":"e_1_3_1_28_2","first-page":"430","article-title":"Using a data metric for preprocessing advice for data mining applications","author":"Engels R.","year":"1998","unstructured":"EngelsR. and TheusingerC., Using a data metric for preprocessing advice for data mining applications, ECAI (1998), 430\u2013434.","journal-title":"ECAI"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/34.809107"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-87656-4_57"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-36182-0_14"},{"key":"e_1_3_1_32_2","first-page":"743","article-title":"Meta-Learning by Landmarking Various Learning Algorithms","author":"Bernhard P.","year":"2000","unstructured":"BernhardP., HilanB. and ChristopheG.-C., Meta-Learning by Landmarking Various Learning Algorithms, In Seventeenth International Conference on Machine Learning, San Francisco, USA, 2000, 743\u2013750.","journal-title":"Seventeenth International Conference on Machine Learning"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.02.025"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/11805816_25"},{"key":"e_1_3_1_35_2","article-title":"Machine Learning","author":"Breiman L.","year":"2001","unstructured":"BreimanL., Machine Learning, Random Forests, 2001.","journal-title":"Random Forests"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-161062","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-161062","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-161062","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:39:47Z","timestamp":1777455587000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-161062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,14]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,7]]}},"alternative-id":["10.3233\/JIFS-161062"],"URL":"https:\/\/doi.org\/10.3233\/jifs-161062","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,3,14]]}}}