scholarly journals Performance Analysis of Rough Set–Based Hybrid Classification Systems in the Case of Missing Values

2021 ◽  
Vol 11 (4) ◽  
pp. 307-318
Author(s):  
Robert K. Nowicki ◽  
Robert Seliga ◽  
Dariusz Żelasko ◽  
Yoichi Hayashi
Keyword(s):  
Performance Analysis ◽  
Rough Set ◽  
Missing Values ◽  
Rough Set Theory ◽  
Classification Systems ◽  
Support Vector ◽  
Vector Machines ◽  
Hybrid Solutions ◽  
Hybrid Classification ◽  
The University

Abstract The paper presents a performance analysis of a selected few rough set–based classification systems. They are hybrid solutions designed to process information with missing values. Rough set-–based classification systems combine various classification methods, such as support vector machines, k–nearest neighbour, fuzzy systems, and neural networks with the rough set theory. When all input values take the form of real numbers, and they are available, the structure of the classifier returns to a non–rough set version. The performance of the four systems has been analysed based on the classification results obtained for benchmark databases downloaded from the machine learning repository of the University of California at Irvine.

Combination with Machine Learning Algorithms for the Classification in E-Bussiness

2011 ◽  
Vol 230-232 ◽  
pp. 625-628
Author(s):  
Lei Shi ◽  
Xin Ming Ma ◽  
Xiao Hong Hu
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Mathematical Tool ◽  
Vector Machines

E-bussiness has grown rapidly in the last decade and massive amount of data on customer purchases, browsing pattern and preferences has been generated. Classification of electronic data plays a pivotal role to mine the valuable information and thus has become one of the most important applications of E-bussiness. Support Vector Machines are popular and powerful machine learning techniques, and they offer state-of-the-art performance. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information and one of its important applications is feature selection. In this paper, rough set theory and support vector machines are combined to construct a classification model to classify the data of E-bussiness effectively.

KNNHI: Resilient KNN algorithm for heterogeneous incomplete data classification and K identification using rough set theory

2022 ◽  
pp. 016555152110695
Author(s):  
Ahmed Hamed ◽  
Mohamed Tahoun ◽  
Hamed Nassar
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Structural Damage ◽  
Missing Values ◽  
Rough Set Theory ◽  
Data Classification ◽  
Recall Accuracy ◽  
K Value ◽  
Almost All ◽  
The University

The original K-nearest neighbour ( KNN) algorithm was meant to classify homogeneous complete data, that is, data with only numerical features whose values exist completely. Thus, it faces problems when used with heterogeneous incomplete (HI) data, which has also categorical features and is plagued with missing values. Many solutions have been proposed over the years but most have pitfalls. For example, some solve heterogeneity by converting categorical features into numerical ones, inflicting structural damage. Others solve incompleteness by imputation or elimination, causing semantic disturbance. Almost all use the same K for all query objects, leading to misclassification. In the present work, we introduce KNNHI, a KNN-based algorithm for HI data classification that avoids all these pitfalls. Leveraging rough set theory, KNNHI preserves both categorical and numerical features, leaves missing values untouched and uses a different K for each query. The end result is an accurate classifier, as demonstrated by extensive experimentation on nine datasets mostly from the University of California Irvine repository, using a 10-fold cross-validation technique. We show that KNNHI outperforms six recently published KNN-based algorithms, in terms of precision, recall, accuracy and F-Score. In addition to its function as a mighty classifier, KNNHI can also serve as a K calculator, helping KNN-based algorithms that use a single K value for all queries that find the best such value. Sure enough, we show how four such algorithms improve their performance using the K obtained by KNNHI. Finally, KNNHI exhibits impressive resilience to the degree of incompleteness, degree of heterogeneity and the metric used to measure distance.

scholarly journals Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
N. Pérez-Díaz ◽  
D. Ruano-Ordás ◽  
F. Fdez-Riverola ◽  
J. R. Méndez
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Wide Range ◽  
Vector Machines ◽  
Bayes Algorithm

Nowadays, spam deliveries represent a major problem to benefit from the wide range of Internet-based communication forms. Despite the existence of different well-known intelligent techniques for fighting spam, only some specific implementations of Naïve Bayes algorithm are finally used in real environments for performance reasons. As long as some of these algorithms suffer from a large number of false positive errors, in this work we propose a rough set postprocessing approach able to significantly improve their accuracy. In order to demonstrate the advantages of the proposed method, we carried out a straightforward study based on a publicly available standard corpus (SpamAssassin), which compares the performance of previously successful well-known antispam classifiers (i.e., Support Vector Machines, AdaBoost, Flexible Bayes, and Naïve Bayes) with and without the application of our developed technique. Results clearly evidence the suitability of our rough set postprocessing approach for increasing the accuracy of previous successful antispam classifiers when working in real scenarios.

scholarly journals Performance of the Hybrid Approach based on Rough Set Theory

2020 ◽  
pp. 217-224
Author(s):  
Betul Kan Kilinc ◽  
Yonca YAZIRLI
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Multinomial Logistic Regression ◽  
Hybrid Approach ◽  
Support Vector ◽  
Learning Methods ◽  
Data Set ◽  
Vector Machines ◽  
Improved Accuracy

One of the essential problems in data mining is the removal of negligible variables from the data set. This paper proposes a hybrid approach that uses rough set theory based algorithms to reduct the attribute selected from the data set and utilize reducts to raise the classification success of three learning methods; multinomial logistic regression, support vector machines and random forest using 5-fold cross validation. The performance of the hybrid approach is measured by related statistics. The results show that the hybrid approach is effective as its improved accuracy by 6-12% for the three learning methods.

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