Comparative analysis of classification algorithms on three different datasets using WEKA

Duriqi, Rafet; Raca, Vigan; Cico, Betim

doi:10.1109/meco.2016.7525775

Cited by 17 publications

(11 citation statements)

References 3 publications

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“…There is a large number of compressive classification algorithms (Duriqi, 2016), but in this paper, a comparative analysis carried out using four classifier algorithms to choose the proper classification algorithm in future research. DNN, RF, SVM, and DT were compared in terms of accuracy and sensitivity rates.…”

Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Classifiers for Education Case Study

Malik¹,

Othman²,

Yusuf³

2019

IJSECS

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Recently, classification is becoming a very valuable tool where a large amount of data is used on a wide range of decisions for the education sector. Classification is a method that used to group data based on predetermined characteristics. It is utilized to classify the item as indicated by the features for the predefined set of classes. The main significance of classification is to classify data from large datasets to find patterns out of it. Nevertheless, it is very important to choose the best classification algorithm which is also called as the classifier. Therefore, this research aims to conduct comparative evaluation between four classifiers which are Deep Neural Network (DNN), Random Forest (RF), Support Vector Machine (SVM) and Decision Tree (DT). All these classifiers have its own efficiency and have an important role in identifying the set of populations based on the training datasets. To choose the best classifiers among the four classifiers, the classifiers performance is required to be evaluated based on the performance metrics. The performance metrics of these classifiers were determined using accuracy and sensitivity rates. This study used education case study on student's performance data for two subjects, Mathematics and Portuguese from two Portugal secondary schools and data on the student's knowledge of Electrical DC Machines subject. After comparing the accuracy and sensitivity rates, DNN has the highest accuracy and sensitivity rate of classification and can be used to further the educationbased research in future.

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Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Classifiers for Education Case Study

Malik¹,

Othman²,

Yusuf³

2019

IJSECS

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“…The final results are good. Duriqi R., Raca V. and Cico B., [3] have worked on three different datasets and showed which classification algorithm is best suited for the particular datasets. The classification algorithms are implemented on the WEKA tool.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Bayes Net Classifier, Naive Bayes Classifier and Combination of both Classifiers using WEKA

Nakra¹,

Duhan²

2019

IJITCS

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Authors here tried to use the WEKA tool to evaluate the performance of various classifiers on a dataset to come out with the optimum classifier, for a particular application. A Classifier is an important part of any machine learning application. It is required to classify various classes and get to know whether the predicted class lies in the true class. There are various performance analysis measures to judge the efficiency of a classifier and there are many tools which provide oodles of classifiers. In the present investigation, Bayes Net, Naive Bayes and their combination have been implemented using WEKA. It has been concluded that the combination of Bayes Net and Naive Bayes provides the maximum classification efficiency out of these three classifiers. Such a hybridization approach will always motivate for combining different classifiers to get the best results.

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“…Finally, the WEKA platform, developed by the University of Waikato in New Zealand, is powerful Machine learning (ML) tool for processing large collections of datasets. Various studies on different datasets [9] [10] [11] have been conducted using this tool which is provided as open source software, under a General Public License.…”

Section: Related Workmentioning

confidence: 99%

“…The rainfall dataset has the following classifications: 0mm as 'DRY', less than 0.4mm as "Drizzle", between 0.4mm to 4mm as "Moderate rain", above 4mm as "Heavy rain". The classification coding used is according to the ones defined by the MetOffice 9 . It has observed that there are: one reading made per day in daily rainfall dataset and two readings made per day in temperature dataset, with, first reading was made at 09:00 and the second at 21:00.…”

Section: B Met Office Ceda Rainfall Datasetsmentioning

confidence: 99%

“…The pre-processing procedures were based on the assumption that the weather related readings were applicable to all TfL bike routes in the city used in the study. For rainfall, 9 Rainfall Classifications -http://www.metoffice.gov.uk/media/pdf/f/c/Fact_sheet_No._3.pdf given there is only one reading per day, the pre-processing procedure included finding a match to dates between the two datasets. For temperature however, it is worth noting that the readings made at 21:00 were mostly higher than those made at 09:00, thus the pro-processing procedures included splitting a day into two 12-hour periods and associating the day-journeys to 21:00 reading and evening-journeys to 09:00 reading.…”

Section: Datasets Pre-processing and Attributesmentioning

confidence: 99%

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Understanding and personalising smart city services using machine learning, The Internet-of-Things and Big Data

Chin

Callaghan

Lam

2017

2017 IEEE 26th International Symposium on Industrial Electronics (ISIE)

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This paper explores the potential of Machine Learning (ML) and Artificial Intelligence (AI) to lever Internet of Things (IoT) and Big Data in the development of personalised services in Smart Cities. We do this by studying the performance of four well-known ML classification algorithms (Bayes Network (BN), Naïve Bayesian (NB), J48, and Nearest Neighbour (NN)) in correlating the effects of weather data (especially rainfall and temperature) on short journeys made by cyclists in London. The performance of the algorithms was assessed in terms of accuracy, trustworthy and speed. The data sets were provided by Transport for London (TfL) and the UK MetOffice. We employed a random sample of some 1,800,000 instances, comprising six individual datasets, which we analysed on the WEKA platform. The results revealed that there were a high degree of correlations between weather-based attributes and the Big Data being analysed. Notable observations were that, on average, the decision tree J48 algorithm performed best in terms of accuracy while the kNN IBK algorithm was the fastest to build models. Finally we suggest IoT Smart City applications that may benefit from our work

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Comparative analysis of classification algorithms on three different datasets using WEKA

Cited by 17 publications

References 3 publications

Comparative Analysis of Classifiers for Education Case Study

Comparative Analysis of Classifiers for Education Case Study

Comparative Analysis of Bayes Net Classifier, Naive Bayes Classifier and Combination of both Classifiers using WEKA

Understanding and personalising smart city services using machine learning, The Internet-of-Things and Big Data

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