A new concept of higher-order similarity and the role of distance/similarity measures in local classification methods

Chemometrics and Intelligent Laboratory Systems

Volumn 157, Issue , 2016, Pages 50-57

  Todeschini, Roberto ^a   Ballabio, Davide ^a   Consonni, Viviana ^a   Grisoni, Francesca ^a  

^a UNIVERSITY OF MILANO BICOCCA   (Italy)

Author keywords

BNN; Classification; Distance measures; KNN; Meta distances; N3; Similarity measures

Indexed keywords

ARTICLE; BINNED NEAREST NEIGHBOR; CLASSIFICATION ALGORITHM; COMPUTER PROGRAM; DISTANCE MEASURE; EUCLIDEAN DISTANCE; HIGHER ORDER SIMILARITY; K NEAREST NEIGHBOR; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL PARAMETERS; MATHEMATICAL PHENOMENA; METADISTANCE; METASIMILARITY; N NEAREST NEIGHBOR; NON ERROR RATE; NONHUMAN; PRIMARY DISTANCE MEASURE; PRIORITY JOURNAL; SIMILARITY MEASURE;

EID: 84976863803     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2016.06.013     Document Type: Article

References (52)

1. [1] Todeschini, R., Ballabio, D., Consonni, V., Distances and other dissimilarity measures in chemometrics. Encyclopedia of Analytical Chemistry, 2015, John Wiley & Sons, Ltd.
2. [2] Todeschini, R., Consonni, V., Xiang, H., Holliday, J., Buscema, M., Willett, P., Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real data sets. J. Chem. Inf. Model. 52:11 (Nov. 2012), 2884–2901.
3. [3] Choi, S.-S., Cha, S.-H., Tappert, C.C., A survey of binary similarity and distance measures. J. Syst. Cybern. Inform. 8:1 (2010), 43–48.
4. [5] Cassotti, M., Consonni, V., Mauri, A., Ballabio, D., Validation and extension of a similarity-based approach for prediction of acute aquatic toxicity towards Daphnia magna. SAR QSAR Environ. Res. 25:12 (Dec. 2014), 1013–1036.
5. [6] Nembri, S., Grisoni, F., Consonni, V., Todeschini, R., In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9. Int. J. Mol. Sci., vol. 17(no. 6), Jun. 2016, 914.
6. [7] Jee, M., Oils and Fats Authentication. 2002, CRC Press, Reading, UK.
7. [8] Tang, C., Heymann, H., Multidimensional sorting, similarity scaling and free-choice profiling of grape jellies. J. Sens. Stud. 17:6 (Dec. 2002), 493–509.
8. [9] Casal, S., Oliveira, M.B.P.P., Alves, M.R., Ferreira, M.A., Discriminate analysis of roasted coffee varieties for trigonelline, nicotinic acid, and caffeine content. J. Agric. Food Chem. 48:8 (Aug. 2000), 3420–3424.
9. [10] Todeschini, R., Ballabio, D., Consonni, V., Sahigara, F., Filzmoser, P., Locally centred Mahalanobis distance: a new distance measure with salient features towards outlier detection. Anal. Chim. Acta 787 (Jul. 2013), 1–9.
10. [11] Egan, W.J., Morgan, S.L., Outlier detection in multivariate analytical chemical data. Anal. Chem. 70:11 (Jun. 1998), 2372–2379.
11. [12] Knorr, E.M., Ng, R.T., Tucakov, V., Distance-based outliers: algorithms and applications. VLDB Journal — Int. J. Very Large Data Bases 8:3–4 (2000), 237–253.
12. [13] Guha, R., Dutta, D., Jurs, P.C., Chen, T., R-NN curves: an intuitive approach to outlier detection using a distance based method. J. Chem. Inf. Model. 46:4 (Jul. 2006), 1713–1722.
13. [14] Eckert, H., Bajorath, J., Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov. Today 12:5–6 (Mar. 2007), 225–233.
14. [15] Koch, M.A., Waldmann, H., Protein structure similarity clustering and natural product structure as guiding principles in drug discovery. Drug Discov. Today 10:7 (Apr. 2005), 471–483.
15. [16] Willett, P., Barnard, J.M., Downs, G.M., Chemical similarity searching. J. Chem. Inf. Comput. Sci. 38:6 (Nov. 1998), 983–996.
16. [17] Cover, T., Hart, P., Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13:1 (Jan. 1967), 21–27.
17. [18] Todeschini, R., Ballabio, D., Cassotti, M., Consonni, V., N3 and BNN: two new similarity based classification methods in comparison with other classifiers. J. Chem. Inf. Model. 55:11 (2015), 2365–2374.
18. [19] Todeschini, R., Ballabio, D., Consonni, V., Distances and other dissimilarity measures in chemometrics. Encyclopedia of Analytical Chemistry, 2015, John Wiley & Sons, Ltd.
19. [20] Fisher, R.A., The use of multiple measurements in taxonomic problems. Ann. Eugenics 7:2 (1936), 179–188.
20. [21] Forina, M., Armanino, C., Castino, M., Ubigli, M., Multivariate data analysis as a discriminating method of the origin of wines. Vitis 25:3 (1986), 189–201.
21. [22] Todeschini, R., Artificial Dataset. 2005, University of Milano-Bicocca.
22. [23] Forina, M., Armanino, C., Lanteri, S., Tiscornia, E., Classification of olive oils from their fatty acid composition. Food Res. Data Anal., 1983, 189–214.
23. [24] Miyashita, Y., Takahashi, Y., Takayama, C., Ohkubo, T., Funatsu, K., Sasaki, S.-I., Computer-assisted structure/taste studies on sulfamates by pattern recognition methods. Anal. Chim. Acta 184 (1986), 143–149.
24. [25] Smith, J.W., Everhart, J., Dickson, W., Knowler, W., Johannes, R., Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. Proceedings of the Annual Symposium on Computer Application in Medical Care, 1988, 261.
25. [26] Baggerly, K.A., Morris, J.S., Edmonson, S.R., Coombes, K.R., Signal in noise: evaluating reported reproducibility of serum proteomic tests for ovarian cancer. J. Natl. Cancer Inst. 97:4 (Feb. 2005), 307–309.
26. [27] Berthonnaud, E., Dimnet, J., Roussouly, P., Labelle, H., Analysis of the sagittal balance of the spine and pelvis using shape and orientation parameters. J. Spinal Disord. Tech. 18:1 (2005), 40–47.
27. [28] Alvarez-Guerra, M., Ballabio, D., Amigo, J.M., Viguri, J.R., Bro, R., A chemometric approach to the environmental problem of predicting toxicity in contaminated sediments. J. Chemom. 24:7–8 (Jul. 2010), 379–386.
28. [29] Mansouri, K., Ringsted, T., Ballabio, D., Todeschini, R., Consonni, V., Quantitative structure–activity relationship models for ready biodegradability of chemicals. J. Chem. Inf. Model. 53:4 (Apr. 2013), 867–878.
29. [30] Todeschini, R., Ballabio, D., Consonni, V., Mauri, A., Pavan, M., CAIMAN (classification and influence matrix analysis): a new approach to the classification based on leverage-scaled functions. Chemom. Intell. Lab. Syst. 87:1 (May 2007), 3–17.
30. [31] Ballabio, D., Consonni, V., Costa, F., Relationships between apple texture and rheological parameters by means of multivariate analysis. Chemom. Intell. Lab. Syst. 111:1 (2012), 28–33.
31. [32] M. Forina, Tobacco Data Set; University of Genoa.
32. [33] Johnson, R.A., Wichern, D.W., et al. Applied Multivariate Statistical Analysis. vol. 4, 1992, Prentice Hall, Englewood Cliffs, NJ.
33. [34] Armanino, C., Lanteri, S., Forina, M., Balsamo, A., Migliardi, M., Cenderelli, G., Hirsutism: a multivariate approach of feature selection and classification. Chemom. Intell. Lab. Syst. 5:4 (Apr. 1989), 335–341.
34. [35] Fearn, T., Design statistics in pharmacochemistry, Mager, PP. Biometrics, 50, 1994, 317.
35. [36] Saviozzi, A., Lotti, G., Piacenti, D., La composizione amminoacida delle farine di girasole. Riv. Della Soc. Ital. Sci. Aliment. 15 (1986), 437–444.
36. [37] Benito, M.J., Ortiz, M.C., Sánchez, M.S., Sarabia, L.A., Iñiguez, M., Typification of vinegars from Jerez and Rioja using classical chemometric techniques and neural network methods. Analyst 124:4 (Jan. 1999), 547–552.
37. [38] Resmini, P., Pellegrino, L., Bertuccioli, M., Moderni criteri per la valutazione chimico-analitica della tipicità di un formaggio: l'esempio del Parmigiano-Reggiano. Riv. Della Soc. Ital. Sci. Aliment. 15 (1986), 315–326.
38. [39] Ortiz, M.C., Saez, J.A., Palacios, J.L., Typification of alcoholic distillates by multivariate techniques using data from chromatographic analyses. Analyst 118:7 (Jan. 1993), 801–805.
39. [40] Worth, A.P., Cronin, M.T.D., Embedded cluster modelling—a novel method for analysing embedded data sets. Quant. Struct.-Act. Relatsh. 18:3 (Jul. 1999), 229–235.
40. [41] Brodnjak-Vončina, D., Kodba, Z.C., Novič, M., Multivariate data analysis in classification of vegetable oils characterized by the content of fatty acids. Chemom. Intell. Lab. Syst. 75:1 (Jan. 2005), 31–43.
41. [42] Gerrild, P.M., Lantz, R.J., Chemical Analysis of 75 Crude Oil Samples from Pliocene Sand Units, Elk Hills Oil Field, California. 1969, US Geological Survey.
42. [43] Hamilton, L.J., Cross-shelf colour zonation in northern Great Barrier Reef lagoon surficial sediments. Aust. J. Earth Sci. 48:2 (Apr. 2001), 193–200.
43. [44] Habbema, J.D., A stepwise discriminant analysis program using density estimation. Compstat, 1974, 101–110.
44. [45] Armanino, C., Leardi, R., Lanteri, S., Modi, G., Chemometric analysis of Tuscan olive oils. Chemom. Intell. Lab. Syst. 5:4 (Apr. 1989), 343–354.
45. [46] Forina, M., Armanino, C., Lanteri, S., Acidi grassi degli animali acquatici: uno studio chemiometrico. Riv. Soc. Ital. Sci. Aliment. 11 (1982), 15–22.
46. [47] Hastie, T., Tibshirani, R., Friedman, J., The Elements of Statistical Learning. 2009, Springer, New York.
47. [48] MATLAB, R2014a. 2014, The MathWorks Inc., Natick, Massachusetts.
48. [49] Todeschini, R., Grisoni, F., Nembri, S., Weighted power–weakness ratio for multi-criteria decision making. Chemom. Intell. Lab. Syst. 146 (Aug. 2015), 329–336.
49. [50] Grisoni, F., Consonni, V., Nembri, S., Todeschini, R., How to weight Hasse matrices and reduce incompatibilities. Chemom. Intell. Lab. Syst. 147 (Oct. 2015), 95–104.
50. [51] Cassotti, M., Grisoni, F., Nembri, S., Todeschini, R., Application of the Weighted Power-Weakness Ratio (wPWR) as a Fusion Rule in Ligand–Based Virtual Screening. MATCH Comm Math Comp Chem. 76:2 (2016), 359–376.
51. [52] Hasse, H., Martinet, J., Uber die Klassenzahl abelscher Zahlkörper. vol. 1, 1985, Citeseer.
52. [53] Grisoni, F., Consonni, V., Vighi, M., Villa, S., Todeschini, R., Expert QSAR system for predicting the bioconcentration factor under the REACH regulation. Environ. Res. 148 (2016), 507–512.