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2
data/tanveer/nursery/conxuntos.dat
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data/tanveer/nursery/conxuntos.dat
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data/tanveer/nursery/conxuntos_kfold.dat
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data/tanveer/nursery/conxuntos_kfold.dat
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data/tanveer/nursery/le_datos.m
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data/tanveer/nursery/le_datos.m
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printf('lendo problema %s ...\n', problema);
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n_entradas= 8; n_clases= 5; n_fich= 1; fich{1}= 'nursery.data'; n_patrons(1)= 12960;
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n_max= max(n_patrons);
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x = zeros(n_fich, n_max, n_entradas); cl= zeros(n_fich, n_max);
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n_patrons_total = sum(n_patrons); n_iter=0;
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n_val_entrada = [3 5 4 4 3 2 3 3]; max_n_val_entrada=max(n_val_entrada);
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val_entrada=cell(n_entradas, max_n_val_entrada);
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clase={'not_recom', 'recommend', 'very_recom', 'priority', 'spec_prior'};
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f=fopen('valores_entradas.dat', 'r');
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if -1==f
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error('erro en fopen abrindo valores_entradas.dat')
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end
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for i=1:n_entradas
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for j=1:n_val_entrada(i)
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val_entrada{i,j} = fscanf(f,'%s', 1);
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% printf('%s ', val_entrada{i,j})
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end
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% printf('\n')
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end
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fclose(f);
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for i_fich=1:n_fich
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f=fopen(fich{i_fich}, 'r');
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if -1==f
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error('erro en fopen abrindo %s\n', fich{i_fich});
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end
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for i=1:n_patrons(i_fich)
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fprintf(2,'%5.1f%%\r', 100*n_iter++/n_patrons_total);
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for j = 1:n_entradas
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t = fscanf(f,'%s',1);
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for k=1:n_val_entrada(j)
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if strcmp(t, val_entrada{j,k})
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x(i_fich,i,j) = k; break
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end
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end
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end
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t = fscanf(f,'%s',1); % lectura da clase
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for k=1:n_clases
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if strcmp(t, clase{k})
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cl(i_fich,i) = k-1; break
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end
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end
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end
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fclose(f);
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end
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12971
data/tanveer/nursery/nursery.arff
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12971
data/tanveer/nursery/nursery.arff
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data/tanveer/nursery/nursery.cost
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data/tanveer/nursery/nursery.cost
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% Rows Columns
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5 5
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% Matrix elements
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0.0 1.0 1.0 1.0 1.0
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1.0 0.0 1.0 1.0 1.0
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1.0 1.0 0.0 1.0 1.0
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1.0 1.0 1.0 0.0 1.0
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1.0 1.0 1.0 1.0 0.0
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12961
data/tanveer/nursery/nursery.data
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12961
data/tanveer/nursery/nursery.data
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data/tanveer/nursery/nursery.names
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data/tanveer/nursery/nursery.names
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1. Title: Nursery Database
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2. Sources:
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(a) Creator: Vladislav Rajkovic et al. (13 experts)
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(b) Donors: Marko Bohanec (marko.bohanec@ijs.si)
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Blaz Zupan (blaz.zupan@ijs.si)
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(c) Date: June, 1997
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3. Past Usage:
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The hierarchical decision model, from which this dataset is
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derived, was first presented in
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M. Olave, V. Rajkovic, M. Bohanec: An application for admission in
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public school systems. In (I. Th. M. Snellen and W. B. H. J. van de
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Donk and J.-P. Baquiast, editors) Expert Systems in Public
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Administration, pages 145-160. Elsevier Science Publishers (North
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Holland)}, 1989.
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Within machine-learning, this dataset was used for the evaluation
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of HINT (Hierarchy INduction Tool), which was proved to be able to
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completely reconstruct the original hierarchical model. This,
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together with a comparison with C4.5, is presented in
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B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by
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function decomposition. ICML-97, Nashville, TN. 1997 (to appear)
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4. Relevant Information Paragraph:
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Nursery Database was derived from a hierarchical decision model
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originally developed to rank applications for nursery schools. It
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was used during several years in 1980's when there was excessive
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enrollment to these schools in Ljubljana, Slovenia, and the
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rejected applications frequently needed an objective
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explanation. The final decision depended on three subproblems:
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occupation of parents and child's nursery, family structure and
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financial standing, and social and health picture of the family.
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The model was developed within expert system shell for decision
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making DEX (M. Bohanec, V. Rajkovic: Expert system for decision
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making. Sistemica 1(1), pp. 145-157, 1990.).
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The hierarchical model ranks nursery-school applications according
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to the following concept structure:
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NURSERY Evaluation of applications for nursery schools
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. EMPLOY Employment of parents and child's nursery
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. . parents Parents' occupation
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. . has_nurs Child's nursery
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. STRUCT_FINAN Family structure and financial standings
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. . STRUCTURE Family structure
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. . . form Form of the family
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. . . children Number of children
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. . housing Housing conditions
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. . finance Financial standing of the family
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. SOC_HEALTH Social and health picture of the family
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. . social Social conditions
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. . health Health conditions
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Input attributes are printed in lowercase. Besides the target
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concept (NURSERY) the model includes four intermediate concepts:
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EMPLOY, STRUCT_FINAN, STRUCTURE, SOC_HEALTH. Every concept is in
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the original model related to its lower level descendants by a set
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of examples (for these examples sets see
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http://www-ai.ijs.si/BlazZupan/nursery.html).
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The Nursery Database contains examples with the structural
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information removed, i.e., directly relates NURSERY to the eight input
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attributes: parents, has_nurs, form, children, housing, finance,
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social, health.
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Because of known underlying concept structure, this database may be
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particularly useful for testing constructive induction and
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structure discovery methods.
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5. Number of Instances: 12960
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(instances completely cover the attribute space)
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6. Number of Attributes: 8
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7. Attribute Values:
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parents usual, pretentious, great_pret
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has_nurs proper, less_proper, improper, critical, very_crit
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form complete, completed, incomplete, foster
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children 1, 2, 3, more
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housing convenient, less_conv, critical
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finance convenient, inconv
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social non-prob, slightly_prob, problematic
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health recommended, priority, not_recom
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8. Missing Attribute Values: none
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9. Class Distribution (number of instances per class)
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class N N[%]
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------------------------------
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not_recom 4320 (33.333 %)
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recommend 2 ( 0.015 %)
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very_recom 328 ( 2.531 %)
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priority 4266 (32.917 %)
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spec_prior 4044 (31.204 %)
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data/tanveer/nursery/nursery.txt
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data/tanveer/nursery/nursery.txt
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n_entradas= 8
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n_clases= 5
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n_arquivos= 1
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fich1= nursery_R.dat
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n_patrons1= 12960
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n_patrons_entrena= 6480
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n_patrons_valida= 6480
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n_conxuntos= 1
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12961
data/tanveer/nursery/nursery_R.dat
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12961
data/tanveer/nursery/nursery_R.dat
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data/tanveer/nursery/valores_entradas.dat
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data/tanveer/nursery/valores_entradas.dat
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usual pretentious great_pret
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proper less_proper improper critical very_crit
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complete completed incomplete foster
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1 2 3 more
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convenient less_conv critical
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convenient inconv
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non-prob slightly_prob problematic
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recommended priority not_recom
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