Lenses

Donated on 7/31/1990

Database for fitting contact lenses

Dataset Characteristics

Multivariate

Subject Area

Other

Associated Tasks

Classification

Feature Type

Categorical

# Instances

24

# Features

3

Dataset Information

Additional Information

The examples are complete and noise free. The examples highly simplified the problem. The attributes do not fully describe all the factors affecting the decision as to which type, if any, to fit. Notes: --This database is complete (all possible combinations of attribute-value pairs are represented). --Each instance is complete and correct. --9 rules cover the training set.

Has Missing Values?

No

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
idIDIntegerno
ageFeatureCategoricalAgeno
spectacle_prescriptionFeatureCategoricalno
astigmaticFeatureBinaryno
classTargetCategoricalno

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Additional Variable Information

1. age of the patient: (1) young, (2) pre-presbyopic, (3) presbyopic 2. spectacle prescription: (1) myope, (2) hypermetrope 3. astigmatic: (1) no, (2) yes 4. tear production rate: (1) reduced, (2) normal

Class Labels

-- 3 Classes 1 : the patient should be fitted with hard contact lenses, 2 : the patient should be fitted with soft contact lenses, 3 : the patient should not be fitted with contact lenses.

Dataset Files

FileSize
lenses.names1.6 KB
lenses.data408 Bytes
Index111 Bytes

Papers Citing this Dataset

Designing a Rule Based Expert Systems for Contact Lenses Patients

By Ibrahim Aydilek, Abdülkadir Gümüşçü. 2018

Published in International Journal of Information Technology and Computer Science.

Multi Objective Optimization of classification rules using Cultural Algorithms

By Sujatha Srinivasan, Sivakumar Ramakrishnan. 2012

Published in Procedia Engineering.

0 to 2 of 2

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2 citations
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Creators

J. Cendrowska

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