A Noise Tolerant Fine Tuning Algorithm For The Naıve Bayesian Learning Algorithm
| المصدر: | مجلة جامعة الملك سعود - علوم الحاسب والمعلومات |
|---|---|
| الناشر: | جامعة الملك سعود |
| المؤلف الرئيسي: | El Hindi, Khalil (Author) |
| المجلد/العدد: | مج26, ع2 |
| محكمة: | نعم |
| الدولة: |
السعودية |
| التاريخ الميلادي: |
2014
|
| الصفحات: | 237 - 246 |
| DOI: |
10.33948/0584-026-002-007 |
| ISSN: |
1319-1578 |
| رقم MD: | 973105 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | science |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Machine Learning | Naive Bayesian Learning | Noise Handling | Overfitting | Instance Weighing
|
| رابط المحتوى: |
| المستخلص: |
This work improves on the FTNB algorithm to make it more tolerant to noise. The FTNB algorithm augments the Naϊve Bayesian (NB) learning algorithm with a fine tuning stage in an attempt to find better estimations of the probability terms involved. The fine-tuning stage has proved to be effective in improving the classification accuracy of the NB; however, it makes the NB algorithm more sensitive to noise in a training set. This work presents several modifications of the fine tuning stage to make it more tolerant to noise. Our empirical results using 47 data sets indicate that the proposed methods greatly enhance the algorithm tolerance to noise. Furthermore, one of the proposed methods improved the performance of the fine tuning method on many noise-free data sets. |
|---|---|
| ISSN: |
1319-1578 |
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