Missing Value Imputation For Breast Cancer Diagnosis Data Using Tensor Factorization Improved By Enhanced Reduced Adaptive Particle Swarm Optimization
| المصدر: | مجلة جامعة الملك سعود - علوم الحاسب والمعلومات |
|---|---|
| الناشر: | جامعة الملك سعود |
| المؤلف الرئيسي: | Nekouie, Atefeh (Author) |
| مؤلفين آخرين: | Moattar, Mohammad Hossein (Co-Author) |
| المجلد/العدد: | مج31, ع3 |
| محكمة: | نعم |
| الدولة: |
السعودية |
| التاريخ الميلادي: |
2019
|
| الصفحات: | 287 - 294 |
| DOI: |
10.33948/0584-031-003-002 |
| ISSN: |
1319-1578 |
| رقم MD: | 974650 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | science |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Breast Cancer Diagnosis | Tensor Factorization | Particle Swarm Optimization | Bayesian Networks | Support Vector Machine | Chaos
|
| رابط المحتوى: |
| المستخلص: |
Cancer refers to a disease in which a group of cells show uncontrolled growth, invasion and metastasis. Data mining and machine learning are common approaches for clinical diagnosis. An important issue in this filed is that these data often include missing value which reduces the diagnostic accuracy. In this paper, a modified tensor factorization method is used for estimating the missing data. When tensor approach intends to estimate the missing value, there should be a class balance in the dataset and tensor does not properly estimate data in the case of data insufficiency. In the proposed schema, particle swarm optimization algorithm with adaptive adjustment (RAPSO) which is improved by chaotic search is used to solve this problem. In this approach, in order to inhibit the random initialization of the particle swarm algorithm, a distinctive approach is adopted and Bayesian network is used. Finally, the accuracy of disease diagnosis is estimated through different classifiers and RMSE measure. The results suggested that the proposed method is superior to other methods in terms of RMSE, accuracy, sensitivity and specificity. © 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
|---|---|
| ISSN: |
1319-1578 |
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