A Developed Prediction Approach for Small Datasets
| المصدر: | مجلة الدراسات المستدامة |
|---|---|
| الناشر: | الجمعية العلمية للدراسات التربوية المستدامة |
| المؤلف الرئيسي: | Salman, Nuha Ahmed (Author) |
| مؤلفين آخرين: | Hasson, Saad Talib (Co-Author) |
| المجلد/العدد: | مج5, ملحق |
| محكمة: | نعم |
| الدولة: |
العراق |
| التاريخ الميلادي: |
2023
|
| التاريخ الهجري: | 1445 |
| الشهر: | أغسطس |
| الصفحات: | 759 - 774 |
| ISSN: |
2663-2284 |
| رقم MD: | 1399909 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EduSearch |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Small Dataset | Prediction | Machine Learning | Attributes | Correlation
|
| رابط المحتوى: |
عدد مرات التحميل
1
| المستخلص: |
Using small datasets is a very challenging task. Developed approaches are significant to make accurate predictions, rankings and identify relevant attributes with a reliable model. Small dataset may lead to biased or incomplete models. Analyzing and prediction with small datasets is usually more difficult due to the restricted number of data. Identifying the relationships between the small dataset’s attributes, specific attributes may have a greater chance of ranking than others due to their insufficient availability. Machine learning has a flexibility and possibility to explain different prospects for health investigation when dealing with small datasets. This paper will analyze the difficulties in making predictions from small datasets and look at several strategies and algorithms that may be applied to reduce these difficulties and increase prediction accuracy |
|---|---|
| ISSN: |
2663-2284 |
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