Comparison of the accuracy of classification algorithms on three data-sets in data mining: Example of 20 classes

Sanlı, T.; Sıcakyüz, Ç.; Yuregir, Oya H.

doi:10.4314/ijest.v12i3.8

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…Grid search was also utilized to explore additional parameter settings. Another study conducted by Sanlı et al (2020) aimed to evaluate the performance of data mining algorithms on three datasets using the WEKA program. Through expert analysis, it was determined that the dataset containing keywords outperformed the other two datasets.…”

Section: Classification Algorithms For Political Election Predictionmentioning

confidence: 99%

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

Elo,

Ghansah,

Kwaku Kwaa-Aidoo

2024

InformingSciJ

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Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

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Section: Classification Algorithms For Political Election Predictionmentioning

confidence: 99%

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

Elo,

Ghansah,

Kwaku Kwaa-Aidoo

2024

InformingSciJ

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“…The most popular strategy for identifying changes in LULC is the post-classification comparison technique, which is based on supervised maximum likelihood classification. This approach has shown good overall classification accuracy for a range of data[21]. In order to identify where a change has occurred, the post-classification comparison approach compares the respective classes after categorising the photos.…”

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confidence: 99%

Assessment of Land Use and Land Cover Change in Southwest Mauritania, Remote Sensing and GIS Approach

Diallo¹,

Ewemoje²,

Abidine³

2022

ARS

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Most countries' land use and land cover (LULC) are changing dramatically today. Most of these changes are related to the way humans and the environment interact. Various methodologies and data sources have been used in conjunction with remote sensing (RS) to categorize and map changes in LULC. This study used RS and Geographic Information System (GIS) tools to analyze LULC change and transitions from 1984 to 2022 in a tropical forested landscape in southwest Mauritania. Using a suitable and high-quality collection of Landsat satellite images. For the classification and creation of LULC maps for the selected periods, the supervised technique using a maximum likelihood classifier was used. The results indicated that there was a remarkable change in all classes of LULC, with an increase in all classes, except barren land, which had a tremendous decrease of −68.58% for the total study area. Therefore, for the total study area, an increase in agricultural land (221%), water bodies (118.46%), vegetation (57.50%), and built-up areas (14.65%) was observed. We believe that by informing policymakers, environmental managers, and the general public about the current changes, our study will help the region to establish appropriate land use rules that may lead to policy document development.

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Machine learning in materials modeling and design

Keya

Arshad

Tolba

et al. 2023

Fundamentals of Multiscale Modeling of Structural Materials

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Comparison of the accuracy of classification algorithms on three data-sets in data mining: Example of 20 classes

Cited by 9 publications

References 17 publications

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

Critical Review of Stack Ensemble Classifier for the Prediction of Young Adults’ Voting Patterns Based on Parents’ Political Affiliations

Assessment of Land Use and Land Cover Change in Southwest Mauritania, Remote Sensing and GIS Approach

Machine learning in materials modeling and design

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