A chi-square-SVM based pedagogical rule extraction method for microarray data analysis
| Autor: | Mukhtar Damola Salawu, Bilkisu Jimada-Ojuolape, Mudashiru Lateef Olumide, Micheal Olaolu Arowolo, S. O. Abdulsalam, Rafiu Mope Isiaka, Kazeem Alagbe Gbolagade |
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| Rok vydání: | 2020 |
| Předmět: |
Feature Dataset
business.industry Computer science Decision tree Process (computing) Medical diagnosis SVMs Machine learning computer.software_genre Synthetic data Support vector machine ComputingMethodologies_PATTERNRECOGNITION Key (cryptography) Chi-square test Sensitivity (control systems) Artificial intelligence business computer Rule-extraction |
| Popis: | Support Vector Machine (SVM) is currently an efficient classification technique due to its ability to capture nonlinearities in diagnostic systems, but it does not reveal the knowledge learnt during training. It is important to understand of how a decision is reached in the machine learning technology, such as bioinformatics. On the other hand, a decision tree has good comprehensibility; the process of converting such incomprehensible models into an understandable model is often regarded as rule extraction. In this paper we proposed an approach for extracting rules from SVM for microarray dataset by combining the merits of both the SVM and decision tree. The proposed approach consists of three steps; the SVM-CHI-SQUARE is employed to reduce the feature set. Dataset with reduced features is used to obtain SVM model and synthetic data is generated. Classification and Regression Tree (CART) is used to generate Rules as the Last phase. We use breast masses dataset from UCI repository where comprehensibility is a key requirement. From the result of the experiment as the reduced feature dataset is used, the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. We obtained accuracy of 93.53%, sensitivity of 89.58%, specificity of 96.70%, and training time of 3.195 seconds. A comparative analysis is carried out done with other algorithms. |
| Databáze: | OpenAIRE |
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