Comparative Evaluation of Four Multi-Label Classification Algorithms in Classifying Learning Objects

Recently educational system, many features control a student’s performance. Students should be well stimulated to study their education. Motivation leads to interest, interest leads to success in their lives. Appropriate assessment of abilities encourages the students to do better in their education. Data mining is to find out patterns by analyzing a large dataset and apply those patterns to predict the possibility of the future events. Data mining is a very critical field in educational area and it provides high potential for the schools and universities. In data mining, there are various classification techniques with various levels of accuracy. This paper focuses to make comparative evaluation of four classifiers such as J48, Naive Bayesian, Bayesian Network and Decision Stump by using WEKA tool. This study is to investigate and identify the best classification technique to analyze and predict the students’ performance of University of Jordan.

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Comparative Evaluation of Several Classification Algorithms on News Posts Using Reddit Social Network Dataset

Advances in Intelligent Systems and Computing - Advances in Information and Communication ◽

10.1007/978-3-030-73100-7_69 ◽

2021 ◽

pp. 1003-1026

Author(s):

Ahmad Rawashdeh ◽

Mohammad Rawashdeh ◽

Omar Rawashdeh

Keyword(s):

Social Network ◽

Comparative Evaluation ◽

Classification Algorithms

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Dynamic Land Cover Mapping of Urbanized Cities with Landsat 8 Multi-temporal Images: Comparative Evaluation of Classification Algorithms and Dimension Reduction Methods

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8030139 ◽

2019 ◽

Vol 8 (3) ◽

pp. 139 ◽

Cited By ~ 1

Author(s):

Ugur Alganci

Keyword(s):

Land Cover ◽

Dimension Reduction ◽

Comparative Evaluation ◽

Support Vector ◽

Classification Algorithms ◽

Landsat 8 ◽

Data Set ◽

Multi Temporal ◽

Reduction Methods

Uncontrolled and continuous urbanization is an important problem in the metropolitan cities of developing countries. Urbanization progress that occurs due to population expansion and migration results in important changes in the land cover characteristics of a city. These changes mostly affect natural habitats and the ecosystem in a negative manner. Hence, urbanization-related changes should be monitored regularly, and land cover maps should be updated to reflect the current situation. This research presents a comparative evaluation of two classification algorithms, pixel-based support vector machine (SVM) classification and decision-tree-oriented geographic object-based image analysis (GEOBIA) classification, in producing a dynamic land cover map of the Istanbul metropolitan city in Turkey between 2013 and 2017 using Landsat 8 Operational Land Imager (OLI) multi-temporal satellite images. Additionally, the efficiencies of the two data dimension reduction methods are evaluated as part of this research. For dimension reduction, built-up index (BUI) and principal component analysis (PCA) data were calculated for five images during the mentioned period, and the classification algorithms were applied on data stacks for each dimension reduction method. The classification results indicate that the GEOBIA classification of the BUI data set provided the highest accuracy, with a 91.60% overall accuracy and 0.91 kappa value. This combination was followed by the GEOBIA classification of the PCA data set, which highlights the overall efficiency of the GEOBIA over the SVM method. On the other hand, the BUI data set provided more reliable and consistent results for urban expansion classes due to representing physical responses of the surface when compared to the data set of the PCA, which is a spectral transformation method.

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