| Autor: |
Uma Maheswari R |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
JOURNAL OF SCIENCE, COMPUTING AND ENGINEERING RESEARCH. :1-6 |
| ISSN: |
2708-1079 |
| DOI: |
10.46379/jscer.2020.010101 |
| Popis: |
Cancer classification is very important in the field of bioinformatics for diagnosis of cancer cells. Accurate prediction of cancer is very important for providing better treatment and to avoid the additional cost associated with wrong therapy. In recent years for classifying the cancer numbers of methods have been exist. The main objective is to find the smallest set of genes by using machine learning algorithms. The proposed method initially uses Naive Bayes classifier with great flexibility. However, the Naive Bayes is not suitable for the classification of large datasets because of significant computational problems. The Naive Bayes combined with the mid k-means clustering (Km-Naive Bayes) is a fast algorithm developed to accelerate both the training and the prediction of Naive Bayes classifiers by using the cluster centers obtained from the k-means clustering. The new techniques namely weighted Mid K-means-Naive Bayes is implemented to improve accuracy and to reduce misclassification and noise arising from irrelevant genes. The proposed algorithm was evaluated with different classifier algorithms which were applied on the same database. The experimental results achieved the proposed Mid K-means - Naive Bayes classification accuracy has 88.8%. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
