Data Set Characteristics: |
Multivariate |
Number of Instances: |
226 |
Area: |
Life |
Attribute Characteristics: |
Categorical |
Number of Attributes: |
69 |
Date Donated |
1992-08-18 |
Associated Tasks: |
Classification |
Missing Values? |
Yes |
Number of Web Hits: |
106091 |
Source:
Original Version:
(a) Original Owner: Professor Jergen at Baylor College of Medicine
(b) Donor: Bruce Porter (porter '@' fall.cs.utexas.EDU)
Standardized Version:
(a) Donor: Ross Quinlan
Data Set Information:
This database is a standardized version of the original audiology database (see audiology.* in this directory). The non-standard set of attributes have been converted to a standard set of attributes according to the rules that follow.
* Each property that appears anywhere in the original .data or .test file has been represented as a separate attribute in this file.
* A property such as age_gt_60 is represented as a boolean attribute with values f and t.
* In most cases, a property of the form x(y) is represented as a discrete attribute x() whose possible values are the various y's; air() is an example. There are two exceptions:
** when only one value of y appears anywhere, e.g. static(normal). In this case, x_y appears as a boolean attribute.
** when one case can have two or more values of x, e.g. history(..). All possible values of history are treated as separate boolean attributes.
* Since boolean attributes only appear as positive conditions, each boolean attribute is assumed to be false unless noted as true. The value of multi-value discrete attributes taken as unknown ("?") unless a value is specified.
* The original case identifications, p1 to p200 in the .data file and t1 to t26 in the .test file, have been added as a unique identifier attribute.
[Note: in the original .data file, p165 has a repeated specification of o_ar_c(normal); p166 has repeated specification of speech(normal) and conflicting values air(moderate) and air(mild). No other problems with the original data were noted.]
Attribute Information:
age_gt_60: f, t.
air(): mild,moderate,severe,normal,profound.
airBoneGap: f, t.
ar_c(): normal,elevated,absent.
ar_u(): normal,absent,elevated.
bone(): mild,moderate,normal,unmeasured.
boneAbnormal: f, t.
bser(): normal,degraded.
history_buzzing: f, t.
history_dizziness: f, t.
history_fluctuating: f, t.
history_fullness: f, t.
history_heredity: f, t.
history_nausea: f, t.
history_noise: f, t.
history_recruitment: f, t.
history_ringing: f, t.
history_roaring: f, t.
history_vomiting: f, t.
late_wave_poor: f, t.
m_at_2k: f, t.
m_cond_lt_1k: f, t.
m_gt_1k: f, t.
m_m_gt_2k: f, t.
m_m_sn: f, t.
m_m_sn_gt_1k: f, t.
m_m_sn_gt_2k: f, t.
m_m_sn_gt_500: f, t.
m_p_sn_gt_2k: f, t.
m_s_gt_500: f, t.
m_s_sn: f, t.
m_s_sn_gt_1k: f, t.
m_s_sn_gt_2k: f, t.
m_s_sn_gt_3k: f, t.
m_s_sn_gt_4k: f, t.
m_sn_2_3k: f, t.
m_sn_gt_1k: f, t.
m_sn_gt_2k: f, t.
m_sn_gt_3k: f, t.
m_sn_gt_4k: f, t.
m_sn_gt_500: f, t.
m_sn_gt_6k: f, t.
m_sn_lt_1k: f, t.
m_sn_lt_2k: f, t.
m_sn_lt_3k: f, t.
middle_wave_poor: f, t.
mod_gt_4k: f, t.
mod_mixed: f, t.
mod_s_mixed: f, t.
mod_s_sn_gt_500: f, t.
mod_sn: f, t.
mod_sn_gt_1k: f, t.
mod_sn_gt_2k: f, t.
mod_sn_gt_3k: f, t.
mod_sn_gt_4k: f, t.
mod_sn_gt_500: f, t.
notch_4k: f, t.
notch_at_4k: f, t.
o_ar_c(): normal,elevated,absent.
o_ar_u(): normal,absent,elevated.
s_sn_gt_1k: f, t.
s_sn_gt_2k: f, t.
s_sn_gt_4k: f, t.
speech(): normal,good,very_good,very_poor,poor,unmeasured.
static_normal: f, t.
tymp(): a,as,b,ad,c.
viith_nerve_signs: f, t.
wave_V_delayed: f, t.
waveform_ItoV_prolonged: f, t.
indentifier (unique for each instance)
class:
cochlear_unknown,mixed_cochlear_age_fixation,poss_central
mixed_cochlear_age_otitis_media,mixed_poss_noise_om,
cochlear_age,normal_ear,cochlear_poss_noise,cochlear_age_and_noise,
acoustic_neuroma,mixed_cochlear_unk_ser_om,conductive_discontinuity,
retrocochlear_unknown,conductive_fixation,bells_palsy,
cochlear_noise_and_heredity,mixed_cochlear_unk_fixation,
otitis_media,possible_menieres,possible_brainstem_disorder,
cochlear_age_plus_poss_menieres,mixed_cochlear_age_s_om,
mixed_cochlear_unk_discontinuity,mixed_poss_central_om
Relevant Papers:
Bareiss, E. Ray, & Porter, Bruce (1987). Protos: An Exemplar-Based Learning Apprentice. In the Proceedings of the 4th International Workshop on Machine Learning, 12-23, Irvine, CA: Morgan Kaufmann.
[Web Link]
Papers That Cite This Data Set1:
 Vassilis Athitsos and Stan Sclaroff. Boosting Nearest Neighbor Classifiers for Multiclass Recognition. Boston University Computer Science Tech. Report No, 2004-006. 2004. [View Context].
Marcus Hutter and Marco Zaffalon. Distribution of Mutual Information from Complete and Incomplete Data. CoRR, csLG/0403025. 2004. [View Context].
Richard Nock and Marc Sebban and David Bernard. A SIMPLE LOCALLY ADAPTIVE NEAREST NEIGHBOR RULE WITH APPLICATION TO POLLUTION FORECASTING. International Journal of Pattern Recognition and Artificial Intelligence Vol. 2003. [View Context].
Alexander K. Seewald. How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness. ICML. 2002. [View Context].
Alexander K. Seewald and Johann Petrak and Gerhard Widmer. Hybrid Decision Tree Learners with Alternative Leaf Classifiers: An Empirical Study. FLAIRS Conference. 2001. [View Context].
Wai Lam and Kin Keung and Charles X. Ling. PR 1527. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. 2001. [View Context].
Jihoon Yang and Rajesh Parekh and Vasant Honavar. DistAl: An inter-pattern distance-based constructive learning algorithm. Intell. Data Anal, 3. 1999. [View Context].
Mark A. Hall. Department of Computer Science Hamilton, NewZealand Correlation-based Feature Selection for Machine Learning. Doctor of Philosophy at The University of Waikato. 1999. [View Context].
Pedro Domingos. Unifying Instance-Based and Rule-Based Induction. Machine Learning, 24. 1996. [View Context].
Thomas G. Dietterich and Ghulum Bakiri. Solving Multiclass Learning Problems via Error-Correcting Output Codes. CoRR, csAI/9501101. 1995. [View Context].
Geoffrey I. Webb. OPUS: An Efficient Admissible Algorithm for Unordered Search. J. Artif. Intell. Res. (JAIR, 3. 1995. [View Context].
Alexander K. Seewald. Dissertation Towards Understanding Stacking Studies of a General Ensemble Learning Scheme ausgefuhrt zum Zwecke der Erlangung des akademischen Grades eines Doktors der technischen Naturwissenschaften. [View Context].
Geoffrey I Webb. Learning Decision Lists by Prepending Inferred Rules. School of Computing and Mathematics Deakin University. [View Context].
Mohammed Waleed Kadous and Claude Sammut. The University of New South Wales School of Computer Science and Engineering Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series. [View Context].
Mohammed Waleed Kadous. Expanding the Scope of Concept Learning Using Metafeatures. School of Computer Science and Engineering, University of New South Wales. [View Context].
Jerome H. Friedman and Ron Kohavi and Youngkeol Yun. To appear in AAAI-96 Lazy Decision Trees. Statistics Department and Stanford Linear Accelerator Center Stanford University. [View Context].
Alexander K. Seewald. Meta-Learning for Stacked Classification. Austrian Research Institute for Artificial Intelligence. [View Context].
Bernhard Pfahringer and Ian H. Witten and Philip Chan. Improving Bagging Performance by Increasing Decision Tree Diversity. Austrian Research Institute for AI. [View Context].
D. Randall Wilson and Roel Martinez. Improved Center Point Selection for Probabilistic Neural Networks. Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms. [View Context].
Citation Request:
WARNING: This database should be credited to the original owner whenever used for any publication whatsoever.
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