A Comparison of Several Approaches to Missing Attribute Values in Data Mining
By J. Grzymala-Busse, Ming Hu. Rough Sets and Current Trends in Computing, 2000
451 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems
View Paper: https://www.semanticscholar.org/paper/e2865994c80238daa302b4960b6274ca2e145104
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Gustavo E. A. P. A. Batista
- Maria Carolina Monard
- Witold Pedrycz
- Jennifer G. Dy
- Kuttiannan Thangavel
- Vladik Kreinovich
- Lukasz Kurgan
- Andrzej Skowron
In Computational Theory and Mathematics
- Witold Pedrycz
- Kuttiannan Thangavel
- A. Pethalakshmi
- Jennifer G. Dy
- Gustavo E. A. P. A. Batista
- Vladik Kreinovich
- Lukasz Kurgan
- Andrzej Skowron
In Information Systems
- Witold Pedrycz
- Gustavo E. A. P. A. Batista
- Maria Carolina Monard
- Jennifer G. Dy
- Kuttiannan Thangavel
- Vladik Kreinovich
- Lukasz Kurgan
- Andrzej Skowron
Citations in the corpus
- The Treatment of Missing Values and its Effect on Classifier Accuracy (2004, background): “The interest in dealing with missing values has continued with the statistical applications to new areas such as Data Mining [6] and Microarrays [7, 13].”
- A Study of K-Nearest Neighbour as an Imputation Method (2002, result): “study, with other simple methods to treat missing values, concluded that it was one of the best methods [6].”
- An analysis of four missing data treatment methods for supervised learning (2003, methodology): “5 seems to have a good internal algorithm to treat missing values, as shown in [5].”
- Impact of imputation of missing values on classification error for discrete data (2008, methodology): “Grzymala-Busse and Hu [11] investigated the accuracy of classification with learning from examples based on rough sets (LERS) classifier [19] and five imputation methods that include mode, C4.5 and LERS ML based imputations, and two other nontraditional methods.”
- Dimensionality reduction based on rough set theory: A review (2009, background): “Grzymala-Busse and Hu [24] Presented and compared nine different approaches to missing attribute values”
- Handbook of Granular Computing (2008, methodology): “Yamakawa and Furukawa [58] present an algorithm for learning membership functions using a model of the fuzzy neuron.”
- Data Preprocessing for Supervised Leaning (2007, unknown): “The topic has been discussed and analyzed by several researchers in the field of ML [5], [14].”