SVM Classifier: Identify Linear Separability Of NAND And NOR Logic Gates
| Autor: | Ratna Sadashiv Chaudhari, Smita Jaywantrao Ghorpade, Seema Sajanrao Patil |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | ECS Transactions. 107:18281-18292 |
| ISSN: | 1938-6737 1938-5862 |
| DOI: | 10.1149/10701.18281ecst |
| Popis: | Artificial Neural Network plays vital role to resolve several real-life problems. Those problems would be impossible or difficult to solve by human or statistical principles. ANN produces better results on large data set as it has self-learning capabilities. The most frequently used family of ANN is pattern classification. Generally, patterns are obtained from real world and get together based on definite attributes in specific regions. Data is classified based on knowledge by recognizing patterns. This study underlines linear separability of Boolean logic gates according to classification. We considered Boolean functions NAND and NOR for classification task. Our problem statement related to this study is, “There is significant difference between the performance of classifier regarding NAND, NOR Boolean logic gates using Support Vector Machine”. Zoo data set is used to carry out the experiment. Accuracy score is measured using Support Vector Machine (SVM). For actual and predicted data set, classification report and confusion matrix is produced. For classification SVM is emerged as a promising technique. Usually, SVM gives remarkable performance as compare to other Machine Learning classifiers methods. As a result, SVM produces outstanding performance on classification problem. Keywords- Boolean Functions, NAND, NOR, Linear Separability, Support Vector Machine (SVM), Confusion Matrix. |
| Databáze: | OpenAIRE |
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