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Intelligent Data Classification Using Optimized Fuzzy Neural Network and Improved Cuckoo Search Optimization | ||
| Iranian Journal of Fuzzy Systems | ||
| دوره 20، شماره 6، بهمن و اسفند 2023، صفحه 155-169 اصل مقاله (411.61 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2023.44767.7887 | ||
| نویسندگان | ||
| Pramoda Patro* 1؛ Krishna Kumar2؛ G. Suresh Kumar3؛ Aditya Kumar Sahu4 | ||
| 1Department of Engineering Mathematics, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, India, 500075 | ||
| 22Department of Applied Science and Humanities, MIT School of engineering, MIT Art Design and Technology University, Loni Kalbhor, Pune,India | ||
| 3Department of Engineering Mathematics, KoneruLakshmaiah Education Foundation Vaddeswaram, Guntur, Andhra Pradesh, India | ||
| 4Department of Computer science and Engineering, Amrita School of Computing, Amaravati Campus, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, 522503, India | ||
| چکیده | ||
| In data mining, classification is one of the most important steps in predicting the target class. Classification is performed by an improved model in existing work in which feature selection is performed based on the bat optimization method to increase the classification accuracy. And an Enhanced Neural Network is used for classification which includes Intuitive, Interpretable Correlated-Contours fuzzy rules. And an effective model is created based on the extraction of fuzzy rules, where data partitioning is performed via a similarity-based directional component. However, the dataset used for experimentation is noisy as well as incomplete data values. Due to incompleteness, knowledge discovery is obstructed and the result of classification is affected as well. And bat provides very slow convergence and easily falls into local optima. To solve this issue, an improved framework is introduced in which missing value imputation is performed by using k means clustering, and then for feature selection, an improved cuckoo search optimization is used. An enhanced classifier based on fuzzy logic and Alex Net neural network structure (F-ANNS) is used for classification and hybrid Ant Colony Particle Swarm Optimization (HASO) is used for optimizing parameters of the AlexNet neural network classifier. The results show that the proposed work is more effective in precision, recall, accuracy, and f-measure as shown by experimental results. | ||
| کلیدواژهها | ||
| Hybrid Ant Colony Particle Swarm Optimization؛ AlexNet neural network؛ cuckoo search؛ missing data Imputation؛ Artificial Neural Network | ||
| مراجع | ||
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