Analysis of Classification Algorithms on Different Datasets

Singaravelan, S.; Arun, R.; Shunmugam, D. Arun; Soundar, K. Ruba; Mayakrishnan, R.; Murugan, D.

doi:10.32728/ric.2018.42/3

Cited by 5 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results are also compared against another popular method, SVM. Iris results presented in Nasiri and Shakibian (2022) and Singaravelan et al (2018) for example, show the accuracies of 96% and 98%, while Wine results in (Deng et al, 2017; Nasiri & Shakibian, 2022) are 98.73% and 100%, respectively. Comparable performance also can be seen in Breast Cancer and Heart, for example in (Nurhayati, Agustian, & Lubis, 2020) (Breast Cancer—98.07%), (Islam et al, 2020) (Breast Cancer—97.14%), (Panup et al, 2022) (Heart—82.9%) and (Khanom et al, 2020) (Heart—86.89%).…”

Section: Weighted Grcmentioning

confidence: 95%

“…For Cardiotocography dataset, neural network research show the accuracies of 84.2%(Çapa, 2018), 86.38%(Parvathsuman et al, 2019) and 83.12%(Amin et al, 2019).The results are also compared against another popular method, SVM. Iris results presented inNasiri and Shakibian (2022) andSingaravelan et al (2018) for example, show the accuracies of 96% and 98%, while Wine results in(Deng et al, 2017;Nasiri & Shakibian, 2022) are 98.73% and 100%, respectively. Comparable performance also can be seen in Breast Cancer and Heart, for example in(Nurhayati, Agustian, & Lubis, 2020) (Breast Cancer-98.07%), (…”

mentioning

confidence: 98%

See 1 more Smart Citation

An evolving feature weighting framework for radial basis function neural network models

2022

View full text Add to dashboard Cite

Via Granular Computing (GrC), one can create effective computational frameworks for obtaining information from data, motivated by the human perception of combining similar objects. Combining knowledge gained via GrC with a Fuzzy inference engine (Neural-Fuzzy) enable us to develop a transparent system. While weighting variables based on their importance during the iterative data granulation process has been proposed before (W-GrC), there is no work in the literature to demonstrate effectiveness and impact on Type-2 Fuzzy Logic systems (T2-FLS). The main contribution of this paper is to extend W-GrC, for the first time, to both Type-1 and Type-2 models known as Radial Basis Function Neural Network (RBFNN) and General Type-2 Radial Basis Function Neural Network (GT2-RBFNN). The proposed framework is validated using popular datasets: Iris, Wine, Breast Cancer, Heart and Cardiotocography. Results show that with the appropriate selection of feature weight parameter, the new computational framework achieves better classification accuracy outcomes. In addition, we also introduce in this research work an investigation on the modelling structure's interpretability (via Nauck's index) where it is shown that a good balance of interpretability and accuracy can be maintained.

show abstract

Section: Weighted Grcmentioning

confidence: 95%

mentioning

confidence: 98%

An evolving feature weighting framework for radial basis function neural network models

2022

View full text Add to dashboard Cite

show abstract

“…Based on the importance of the logical and knowledge-based approaches, there is a rift between ML and AI. ML mechanism relying on database technology is the extensive use of data technological items [12]. Various problems such as HAR, air quality prediction [21], key sentence extraction using the comments in the blogs [22], detection of emergency situation [23], face recognition for security purpose [24], [30], film review analysis for maximizing the profit of investors and recommendation for viewers [25], intrusion detection [26], [31] can be solved using the ML techniques [27].…”

Section: Introductionmentioning

confidence: 99%

“…12 present the results with 12 fold cross validation. The obtained results indicate that the accuracy decreases as compared to those with 10 fold cross validation.…”

mentioning

confidence: 99%

Comparative analysis of HAR datasets using classification algorithms

et al. 2022

View full text Add to dashboard Cite

In the current research and development era, Human Activity Recognition (HAR) plays a vital role in analyzing the movements and activities of a human being. The main objective of HAR is to infer the current behaviour by extracting previous information. Now-a-days, the continuous improvement of living condition of human beings changes human society dramatically. To detect the activities of human beings, various devices, such as smartphones and smart watches, use different types of sensors, such as multi modal sensors, non-video based and video-based sensors, and so on. Among the entire machine learning approaches, tasks in different applications adopt extensively classification techniques, in terms of smart homes by active and assisted living, healthcare, security and surveillance, making decisions, tele-immersion, forecasting weather, official tasks, and prediction of risk analysis in society. In this paper, we perform three classification algorithms, Sequential Minimal Optimization (SMO), Random Forest (RF), and Simple Logistic (SL) with the two HAR datasets, UCI HAR and WISDM, downloaded from the UCI repository. The experiment described in this paper uses the WEKA tool to evaluate performance with the matrices, Kappa statistics, relative absolute error, mean absolute error, ROC Area, and PRC Area by 10-fold cross validation technique. We also provide a comparative analysis of the classification algorithms with the two determined datasets by calculating the accuracy with precision, recall, and F-measure metrics. In the experimental results, all the three algorithms with the UCI HAR datasets achieve nearly the same accuracy of 98%.The RF algorithm with the WISDM dataset has the accuracy of 90.69%,better than the others.

show abstract

Designing efficient streaming-data processing for intrusion avoidance and detection engines using entity selection and entity attribute approach

Harish,

Selvaraj

2023

SUSTAINABLE DEVELOPMENTS IN MATERIALS SCIENCE, TECHNOLOGY AND ENGINEERING: Sustainable Development in Material Science of Today

View full text Add to dashboard Cite

Analysis of Classification Algorithms on Different Datasets

Abstract: Purpose. Data mining is the forthcoming research area to solve different problems and classification is one of main problem in the field of data mining. In this paper, we use two classification algorithms J48 and Sequential Minimal Optimization alias SMO of the Weka interface.

Cited by 5 publications

References 4 publications

An evolving feature weighting framework for radial basis function neural network models

An evolving feature weighting framework for radial basis function neural network models

Comparative analysis of HAR datasets using classification algorithms

Designing efficient streaming-data processing for intrusion avoidance and detection engines using entity selection and entity attribute approach

Contact Info

Product

Resources

About