openml-datasets

name	type	format
lymphatics	string	default
block_of_affere	string	default
bl_of_lymph_c	string	default
bl_of_lymph_s	string	default
by_pass	string	default
extravasates	string	default
regeneration_of	string	default
early_uptake_in	string	default
lym_nodes_dimin	number	default
lym_nodes_enlar	number	default
changes_in_lym	string	default
defect_in_node	string	default
changes_in_node	string	default
changes_in_stru	string	default
special_forms	string	default
dislocation_of	string	default
exclusion_of_no	string	default
no_of_nodes_in	number	default
class	string	default

Data Files

File	Description	Size	Last modified	Download
lymph		22.5 kB	3 months ago	lymph
lymph		14.7 kB	3 months ago	lymph

Files	Size	Format	Created	Updated	License	Source
2	37.2 kB	arff, csv		about 2 months ago	Open Data Commons Public Domain Dedication and License

The resources for this dataset can be found at https://www.openml.org/d/10

Author:
Source: Unknown -
Please cite:

Citation Request: This lymphography domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. Soklic for providing the data. Please include this citation if you plan to use this database.

Title: Lymphography Domain
Sources: (a) See Above. (b) Donors: Igor Kononenko, University E.Kardelj Faculty for electrical engineering Trzaska 25 61000 Ljubljana (tel.: (38)(+61) 265-161

Bojan Cestnik Jozef Stefan Institute Jamova 39 61000 Ljubljana Yugoslavia (tel.: (38)(+61) 214-399 ext.287) (c) Date: November 1988
Past Usage: (sveral)
1. Cestnik,G., Konenenko,I, & Bratko,I. (1987). Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users. In I.Bratko & N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press. – Assistant-86: 76% accuracy
2. Clark,P. & Niblett,T. (1987). Induction in Noisy Domains. In I.Bratko & N.Lavrac (Eds.) Progress in Machine Learning, 11-30, Sigma Press. – Simple Bayes: 83% accuracy – CN2 (99% threshold): 82%
3. Michalski,R., Mozetic,I. Hong,J., & Lavrac,N. (1986). The Multi-Purpose Incremental Learning System AQ15 and its Testing Applications to Three Medical Domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, 1041-1045. Philadelphia, PA: Morgan Kaufmann. – Experts: 85% accuracy (estimate) – AQ15: 80-82%
Relevant Information: This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. (See also breast-cancer and primary-tumor.)
Number of Instances: 148
Number of Attributes: 19 including the class attribute
Attribute information: — NOTE: All attribute values in the database have been entered as numeric values corresponding to their index in the list of attribute values for that attribute domain as given below.
1. class: normal find, metastases, malign lymph, fibrosis
2. lymphatics: normal, arched, deformed, displaced
3. block of affere: no, yes
4. bl. of lymph. c: no, yes
5. bl. of lymph. s: no, yes
6. by pass: no, yes
7. extravasates: no, yes
8. regeneration of: no, yes
9. early uptake in: no, yes
10. lym.nodes dimin: 0-3
11. lym.nodes enlar: 1-4
12. changes in lym.: bean, oval, round
13. defect in node: no, lacunar, lac. marginal, lac. central
14. changes in node: no, lacunar, lac. margin, lac. central
15. changes in stru: no, grainy, drop-like, coarse, diluted, reticular, stripped, faint,
16. special forms: no, chalices, vesicles
17. dislocation of: no, yes
18. exclusion of no: no, yes
19. no. of nodes in: 0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, >=70
Missing Attribute Values: None
Class Distribution: Class: Number of Instances: normal find: 2 metastases: 81 malign lymph: 61 fibrosis: 4

Relabeled values in attribute 'lymphatics' From: '1' To: normal
From: '2' To: arched
From: '3' To: deformed
From: '4' To: displaced

Relabeled values in attribute 'block_of_affere' From: '1' To: no
From: '2' To: yes

Relabeled values in attribute 'bl_of_lymph_c' From: '1' To: no
From: '2' To: yes

Relabeled values in attribute 'bl_of_lymph_s' From: '1' To: no
From: '2' To: yes

Relabeled values in attribute 'by_pass' From: '1' To: no
From: '2' To: yes

Relabeled values in attribute 'extravasates' From: '1' To: no
From: '2' To: yes

Relabeled values in attribute 'regeneration_of' From: '1' To: no
From: '2' To: yes

Relabeled values in attribute 'early_uptake_in' From: '1' To: no
From: '2' To: yes

Relabeled values in attribute 'changes_in_lym' From: '1' To: bean
From: '2' To: oval
From: '3' To: round

Relabeled values in attribute 'defect_in_node' From: '1' To: no
From: '2' To: lacunar
From: '3' To: lac_margin
From: '4' To: lac_central

Relabeled values in attribute 'changes_in_node' From: '1' To: no
From: '2' To: lacunar
From: '3' To: lac_margin
From: '4' To: lac_central

Relabeled values in attribute 'changes_in_stru' From: '1' To: no
From: '2' To: grainy
From: '3' To: drop_like
From: '4' To: coarse
From: '5' To: diluted
From: '6' To: reticular
From: '7' To: stripped
From: '8' To: faint

Relabeled values in attribute 'special_forms' From: '1' To: no
From: '2' To: chalices
From: '3' To: vesicles

Relabeled values in attribute 'dislocation_of' From: '1' To: no
From: '2' To: yes

Relabeled values in attribute 'exclusion_of_no' From: '1' To: no
From: '2' To: yes

Relabeled values in attribute 'class' From: '1' To: normal
From: '2' To: metastases
From: '3' To: malign_lymph
From: '4' To: fibrosis