Commit Inicial

data/tanveer/nursery/nursery.names

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+1. Title: Nursery Database
+
+2. Sources:
+   (a) Creator: Vladislav Rajkovic et al. (13 experts)
+   (b) Donors: Marko Bohanec   (marko.bohanec@ijs.si)
+               Blaz Zupan      (blaz.zupan@ijs.si)
+   (c) Date: June, 1997
+
+3. Past Usage:
+
+   The hierarchical decision model, from which this dataset is
+   derived, was first presented in 
+
+   M. Olave, V. Rajkovic, M. Bohanec: An application for admission in
+   public school systems. In (I. Th. M. Snellen and W. B. H. J. van de
+   Donk and J.-P. Baquiast, editors) Expert Systems in Public
+   Administration, pages 145-160. Elsevier Science Publishers (North
+   Holland)}, 1989.
+
+   Within machine-learning, this dataset was used for the evaluation
+   of HINT (Hierarchy INduction Tool), which was proved to be able to
+   completely reconstruct the original hierarchical model. This,
+   together with a comparison with C4.5, is presented in
+
+   B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by
+   function decomposition. ICML-97, Nashville, TN. 1997 (to appear)
+
+4. Relevant Information Paragraph:
+
+   Nursery Database was derived from a hierarchical decision model
+   originally developed to rank applications for nursery schools. It
+   was used during several years in 1980's when there was excessive
+   enrollment to these schools in Ljubljana, Slovenia, and the
+   rejected applications frequently needed an objective
+   explanation. The final decision depended on three subproblems:
+   occupation of parents and child's nursery, family structure and
+   financial standing, and social and health picture of the family.
+   The model was developed within expert system shell for decision
+   making DEX (M. Bohanec, V. Rajkovic: Expert system for decision
+   making. Sistemica 1(1), pp. 145-157, 1990.).
+
+   The hierarchical model ranks nursery-school applications according
+   to the following concept structure:
+
+   NURSERY            Evaluation of applications for nursery schools
+   . EMPLOY           Employment of parents and child's nursery
+   . . parents        Parents' occupation
+   . . has_nurs       Child's nursery
+   . STRUCT_FINAN     Family structure and financial standings
+   . . STRUCTURE      Family structure
+   . . . form         Form of the family
+   . . . children     Number of children
+   . . housing        Housing conditions
+   . . finance        Financial standing of the family
+   . SOC_HEALTH       Social and health picture of the family
+   . . social         Social conditions
+   . . health         Health conditions
+
+   Input attributes are printed in lowercase. Besides the target
+   concept (NURSERY) the model includes four intermediate concepts:
+   EMPLOY, STRUCT_FINAN, STRUCTURE, SOC_HEALTH. Every concept is in
+   the original model related to its lower level descendants by a set
+   of examples (for these examples sets see 
+   http://www-ai.ijs.si/BlazZupan/nursery.html).
+
+   The Nursery Database contains examples with the structural
+   information removed, i.e., directly relates NURSERY to the eight input
+   attributes: parents, has_nurs, form, children, housing, finance,
+   social, health.
+
+   Because of known underlying concept structure, this database may be
+   particularly useful for testing constructive induction and
+   structure discovery methods.
+
+5. Number of Instances: 12960
+   (instances completely cover the attribute space)
+
+6. Number of Attributes: 8
+
+7. Attribute Values:
+
+   parents        usual, pretentious, great_pret
+   has_nurs       proper, less_proper, improper, critical, very_crit
+   form           complete, completed, incomplete, foster
+   children       1, 2, 3, more
+   housing        convenient, less_conv, critical
+   finance        convenient, inconv
+   social         non-prob, slightly_prob, problematic
+   health         recommended, priority, not_recom
+
+8. Missing Attribute Values: none
+
+9. Class Distribution (number of instances per class)
+
+   class        N         N[%]
+   ------------------------------
+   not_recom    4320   (33.333 %)
+   recommend       2   ( 0.015 %)
+   very_recom    328   ( 2.531 %)
+   priority     4266   (32.917 %)
+   spec_prior   4044   (31.204 %)