Abstract
The recent interest for tree based methodologies by more and more researchers and users have stimulated an interesting debate about the implemented software and the desired software. A characterisation of the available software suitable for so called classification and regression trees methodology will be described. Furthermore, the general properties that an ideal programme in this domain should have, will be defined. This allows to emphasise the peculiar methodological aspects that a general segmentation procedure should achieve.
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References
AnswerTree (1997). SPSS-AnswerTree, SPSS Inc., Chicago, IL„ USA.
Breiman, L., Friedman, J.H., Olshen, R.A: & Stone, C.J. (1984). Classification and Regression Trees, Wadsworth, Belmont, CA.
Capiluppi C., Fabbris L. & Scarabello M. (1997). UNAIDED: a PC system for Binary and Ternary Segmentation analysis, Book of Short Papers of Classification and Data Analysis, Pescara.
Cappelli, C., Mola, F. & Siciliano, R. (1998). An alternative pruning method based on impurity, In Proceedings of COMPSTAT’98, Bristol August (to appear)
CART (1984). CART: A software for classification and regression trees, California Statistical Software Inc., Yorkshire Ct. Lafayette, CA, USA.
CHAID (1996). SPSS-CHAID, SPSS Inc., Chicago, IL, USA.
Ciampi, A. (1994). Classification and discrimination: The RECPAM approach, In COMPSTAT ‘94, Dutter R. & Grossmann W. eds., 139–147, Physica Verlag, Heidelberg.
Clark, L. A. & Pregibon, D. (1992). Tree based models, In Statistical Models in S, Chambers J.M. & Hastie T.J. eds., Wadswoorth and Brooks/Cole.
FIRM (1990). FIRM: Formal inference-based recursive modelling by D.M. Hawkins (for IBM PC), University of Minnesota, School of Statistics, Technical Report 546.
Hörmann, A. et al. (1990). Comparing statistical analysis systems, Statistical Software Newsletter, 16, 90–127.
Klaschka, J. & Mola, F. (1998). Minimization of computational cost in tree- based methods by a proper ordering of splits, In Proceedings of COMPSTA T ‘98, Bristol August (to appear).
Klaschka, J., Siciliano, R. & Antoch, J. (1998). Computational enhancements in Tree-Growing Methods, in Proceedings of IFCS’98, Rome (to appear).
Kass, G.V. (1980). An exploratory technique for investigating large quantities of categorical data, Applied Statistics, 29, 119–127.
Mola, F. & Siciliano, R. (1992). A two-stage predictive splitting algorithm in binary segmentation, In COMPSTAT’92, Y. Dodge & J. Whittaker eds., 179–184, Physica Verlag, Heidelberg.
Mola, F. & Siciliano, R. (1994). Alternative strategies and CATANOVA testing in TWO-STAGE binary segmentation, New Approaches in Classification and Data Analysis, Diday E. et al. eds., 316–323, Springer Verlag.
Mola, F. & Siciliano, R. (1997). A fast splitting procedure for classification trees, Statistics and Computing, 7, 209–216.
Mola, F. & Siciliano, R. (1997). Visualizing data in tree-structured classification, In Proceedings of the IFCS-96: Data Science, Classification and Related Methods, Hayashi C. et al eds., Springer Verlag, Tokyo.
Mola, F., Klaschka, J. & Siciliano, R. (1996). Logistic classification trees, In COMPSTAT ‘96 Proceedings, Prat A. ed., Physica Verlag, Heidelberg.
Morgan, J.N. & Sonquist, J.A. (1963). Problems in the analysis of survey data and a proposal, JASA, 58, 415–434.
Quinlan, J. R. (1992). C4.5: Programs for machine learning, Morgan Kaufmann, New York.
Safavian, S. R. & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Trans, on Systems, Man. and Cybernetics, 21, 660–674.
Siciliano, R. & Mola, F. (1997). Multivariate data analysis and modeling through classification and regression trees, Proceedings of the Second World Conference of IASC, Wegman E. ed., Pasadena CA (in press).
Siciliano, R. & Mola, F. (1997). Ternary classification trees: a factorial approach, In Visualization of Categorical Data, Greenacre M. & Blasius J. Eds, Academic Press.
SICLA (1991), Manuel de l’utilisateur, INRIA, Le Chesnay Cedex, France.
S-PLUS (1997). S-PLUS Computing Environment v.4.0, StatSci, a division of MathSoft Inc., Seattle, WA, USA.
SPAD.S (1996). Systeme Portable pour l’Analyse des Donnes-Segmentation, v.3.2. CISIA, Saint Mande.
Steinberg, D., & Colla, P. (1995). CART. Salford System, San Diego, CA.
Steinberg,, D. (1996). New developments in CART (Classification & Regression Tree) software, In Proceedings of the Fifth Conference of the IFCS, Kobe, Japan.
Venables, W. N. & Ripley, B. D. (1994). Modern Applied Statistics with S-Plus, Springer-Verlag, Berlin.
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Mola, F. (1998). Classification and Regression Trees Software and New Developments. In: Rizzi, A., Vichi, M., Bock, HH. (eds) Advances in Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72253-0_42
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DOI: https://doi.org/10.1007/978-3-642-72253-0_42
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