A computational model for knowledge extraction in uncertain textual data using karnaugh map technique

Rajput, Dharmendra Singh; Thakur, Ramjeevan Singh; Thakur, Ghanshyam Singh

doi:10.1504/ijcsm.2016.076393

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…One of the important challenges in data mining is handling of imbalanced data in classification. 1–4 We know that classification is an important technique of data mining, in which unknown class samples are assigned to some class based on previous knowledge from training samples. 5,6 Imbalance appears when data are unequally distributed into classes; some classes may have large quantity of data called as majority classes and some may have just few instances of data called minority classes.…”

Section: Introductionmentioning

confidence: 99%

A review on classification of imbalanced data for wireless sensor networks

Patel

Rajput

Reddy

et al. 2020

International Journal of Distributed Sensor Networks

112

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Classification of imbalanced data is a vastly explored issue of the last and present decade and still keeps the same importance because data are an essential term today and it becomes crucial when data are distributed into several classes. The term imbalance refers to uneven distribution of data into classes that severely affects the performance of traditional classifiers, that is, classifiers become biased toward the class having larger amount of data. The data generated from wireless sensor networks will have several imbalances. This review article is a decent analysis of imbalance issue for wireless sensor networks and other application domains, which will help the community to understand WHAT, WHY, and WHEN of imbalance in data and its remedies.

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Section: Introductionmentioning

confidence: 99%

A review on classification of imbalanced data for wireless sensor networks

Patel

Rajput

Reddy

et al. 2020

International Journal of Distributed Sensor Networks

112

View full text Add to dashboard Cite

show abstract

“…According to information from the Ministry of Higher Classifying, unbalanced class data is a significant problem in machine learning and data mining. Because, after all, causes inaccuracy in classification is the imbalance of class data [14,15]. It happened because the imbalance distribution of class data causes biased classifier performance due to misclassifying the minority class or minority classes not being considered in the overall classification results [16].…”

Section: Introductionmentioning

confidence: 99%

Improving SVM Classification Performance on Unbalanced Student Graduation Time Data Using SMOTE

Anggrawan¹,

Hairani²,

Satria³

2023

IJIET

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Student graduation accuracy is one of the indicators of the success of higher education institutions in carrying out the teaching and learning process and as a component of higher education accreditation. So it is not surprising that building a system that can predict or classify students graduating on time or not on time is necessary for universities to monitor the exact number of students graduating on time using educational technology. Unfortunately, educational technology or machine learning with data mining approaches is less accurate in classifying classes with unbalanced data. Therefore, this research purpose is to build a machine learning system that can improve classification performance on unbalanced class data between students who graduate on time and graduate late. This study applies the Synthetic Minority Oversampling Technique (SMOTE) method to improve the classifying performance of the Support Vector Machine (SVM) data mining method. The results of the study concluded that using the SMOTE method increased the accuracy, precision, and sensitivity of the SVM method in classifying class data of unbalanced student graduation times. The SVM performance score rises by 3% for classification accuracy, 8% for classification precision, and 25% for classification sensitivity.

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Feature selection analysis for multimedia event detection

Rajput

Singh

Agarwal

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. Selection of attributes has been the current emerging research area for a long while and much work has been finished on. With the making of tremendous dataset and the resulting necessities for good machine learning systems, new issues emerge and ways to deal with feature selection are the area of research and in need. Preprocessed and generated datasets of multimedia event detection with numerical values is the input for our research. This paper recognizes the contribution of each attribute and combinational attributes towards the accuracy of the classifier model. The analysis is done using LIBSVM tool and different kernel modes using Support Vector Machine (SVM) classifier. After the analysis is done, we will get to know that which attribute yields the maximum accuracy in classifying the instances.

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A computational model for knowledge extraction in uncertain textual data using karnaugh map technique

Cited by 5 publications

References 20 publications

A review on classification of imbalanced data for wireless sensor networks

A review on classification of imbalanced data for wireless sensor networks

Improving SVM Classification Performance on Unbalanced Student Graduation Time Data Using SMOTE

Feature selection analysis for multimedia event detection

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