Analysis of classification learning algorithms
The paper attempts to apply data mining Technique, Five classification algorithms were used to build data they are (ZeroR, SMO, Naive Bayesian, J48 and Random Forest).The analysis implemented using WEKA (3.8.2) Data mining software tool. The information was collected from college of Information Engi...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2020-02-01.
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| LEADER | 02501 am a22003133u 4500 | ||
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| 001 | 0 nhttps:__ijeecs.iaescore.com_index.php_IJEECS_article_downloadSuppFile_19798_2713 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Esmaeel, Hana Rashied |e author |
| 100 | 1 | 0 | |e contributor |
| 245 | 0 | 0 | |a Analysis of classification learning algorithms |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-02-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/19798 | ||
| 520 | |a The paper attempts to apply data mining Technique, Five classification algorithms were used to build data they are (ZeroR, SMO, Naive Bayesian, J48 and Random Forest).The analysis implemented using WEKA (3.8.2) Data mining software tool. The information was collected from college of Information Engineering (COIE) In Al Nahrain University within the variety of form using "Referendum" to estimate the teacher performance; it was store in Excel file CSV format then regenerate to ARFF (Attribute Relation File Format). Many criteria like (Time taken to create models, accuracy and average error) was taken to evaluate the algorithms Random forest and , SMO Predicts higher than alternative algorithms ,since their accuracy is the highest and have lowest average error compared to others ,"The teacher clarification and wanting to be useful to students " was the strongest attribute. Further removing the bad ranked attributes (10, 11, 12, and 14) that have a lower contact on dataset can increase accuracies of algorithms | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Data mining, Weka, Decision tree, classification, teacher evaluation | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 17, No 2: February 2020; 1029-1039 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v17.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/19798/13556 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/downloadSuppFile/19798/2713 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/19798/13556 |z Get fulltext |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/downloadSuppFile/19798/2713 |z Get fulltext |