Classification of Incomplete Data Using Autoencoder and Evidential Reasoning

2019 ◽  
pp. 167-177
Author(s):  
Suvra Jyoti Choudhury ◽  
Nikhil R. Pal
Keyword(s):  
Incomplete Data ◽  
Evidential Reasoning

scholarly journals A Robust Classification of Galaxy Spectra: Dealing with Noisy and Incomplete Data

1999 ◽  
Vol 117 (5) ◽  
pp. 2052-2062 ◽  
Author(s):  
A. J. Connolly ◽  
A. S. Szalay
Keyword(s):  
Incomplete Data ◽  
Robust Classification

Handling incomplete data classification using imputed feature selected bagging (IFBag) method

2021 ◽  
Vol 25 (4) ◽  
pp. 825-846
Author(s):  
Ahmad Jaffar Khan ◽  
Basit Raza ◽  
Ahmad Raza Shahid ◽  
Yogan Jaya Kumar ◽  
Muhammad Faheem ◽  
...  
Keyword(s):  
Multiple Imputation ◽  
Ensemble Learning ◽  
Incomplete Data ◽  
Missing Values ◽  
Learning Approach ◽  
Imputation Methods ◽  
Real World Datasets ◽  
Almost All ◽  
Bagging Ensemble

Almost all real-world datasets contain missing values. Classification of data with missing values can adversely affect the performance of a classifier if not handled correctly. A common approach used for classification with incomplete data is imputation. Imputation transforms incomplete data with missing values to complete data. Single imputation methods are mostly less accurate than multiple imputation methods which are often computationally much more expensive. This study proposes an imputed feature selected bagging (IFBag) method which uses multiple imputation, feature selection and bagging ensemble learning approach to construct a number of base classifiers to classify new incomplete instances without any need for imputation in testing phase. In bagging ensemble learning approach, data is resampled multiple times with substitution, which can lead to diversity in data thus resulting in more accurate classifiers. The experimental results show the proposed IFBag method is considerably fast and gives 97.26% accuracy for classification with incomplete data as compared to common methods used.

scholarly journals Rough Support Vector Machine for Classification with Interval and Incomplete Data

2020 ◽  
Vol 10 (1) ◽  
pp. 47-56 ◽  
Author(s):  
Robert K. Nowicki ◽  
Konrad Grzanek ◽  
Yoichi Hayashi
Keyword(s):  
Support Vector Machine ◽  
Rough Sets ◽  
Incomplete Data ◽  
Input Data ◽  
Missing Values ◽  
Classification Scheme ◽  
Equivalence Classes ◽  
Support Vector ◽  
Positive Boundary

AbstractThe paper presents the idea of connecting the concepts of the Vapnik’s support vector machine with Pawlak’s rough sets in one classification scheme. The hybrid system will be applied to classifying data in the form of intervals and with missing values [1]. Both situations will be treated as a cause of dividing input space into equivalence classes. Then, the SVM procedure will lead to a classification of input data into rough sets of the desired classes, i.e. to their positive, boundary or negative regions. Such a form of answer is also called a three–way decision. The proposed solution will be tested using several popular benchmarks.

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