random.forest.SDA

Random forest algorithm for optimal split based decision tree for symbolic objects

decision tree

Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation.
(Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>,
Breiman, L. (1996), <doi:10.1007/BF00058655>,
Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>,
Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>,
Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>,
Calinski, T., Harabasz, J. (1974) <doi:10.1080/03610927408827101>,
Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>,
Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>,
Walesiak, M., Dudek, A. (2008) <doi:10.1007/978-3-540-78246-9_11>,
Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).

Andrzej Dudek

symbolicDA

Analysis of Symbolic Data

random.forest.SDA function

<dl><dt>sdt</dt>
<dd>Symbolic data table</dd><dt>formula</dt>
<dd>formula as in ln function</dd><dt>testSet</dt>
<dd>a vector of integers indicating classes to which each objects are allocated in learnig set</dd><dt>mfinal</dt>
<dd>number of partial models generated</dd><dt>...</dt>
<dd>arguments passed to decisionTree.SDA function</dd></dl>

Arguments

Andrzej Dudek <a href="/link/andrzej.dudek%40ue.wroc.pl?package=symbolicDA&version=0.7-1" data-mini-rdoc="symbolicDA::andrzej.dudek@ue.wroc.pl">andrzej.dudek@ue.wroc.pl</a>
Marcin Pełka <a href="/link/marcin.pelka%40ue.wroc.pl?package=symbolicDA&version=0.7-1" data-mini-rdoc="symbolicDA::marcin.pelka@ue.wroc.pl">marcin.pelka@ue.wroc.pl</a>
Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland <a href="http://keii.ue.wroc.pl/symbolicDA/">http://keii.ue.wroc.pl/symbolicDA/</a>

Author

Random forest algorithm for optimal split based decision tree for symbolic objects — random.forest.SDA

<dl>

<dt>sdt</dt>
<dd>Symbolic data table</dd>

<dt>formula</dt>
<dd>formula as in ln function</dd>

<dt>testSet</dt>
<dd>a vector of integers indicating classes to which each objects are allocated in learnig set</dd>

<dt>mfinal</dt>
<dd>number of partial models generated</dd>

<dt>...</dt>
<dd>arguments passed to decisionTree.SDA function</dd>

</dl>

Andrzej Dudek <a href='mailto:andrzej.dudek@ue.wroc.pl'>andrzej.dudek@ue.wroc.pl</a>
Marcin Pełka <a href='mailto:marcin.pelka@ue.wroc.pl'>marcin.pelka@ue.wroc.pl</a>
Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland <a href='http://keii.ue.wroc.pl/symbolicDA/'>http://keii.ue.wroc.pl/symbolicDA/</a>

random.forest.SDA: Random forest algorithm for optimal split based decision tree for symbolic objects

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples