مدل طبقه بندي مبتني بر پرش قابل اعتماد و بهره‌گيري در داده‌كاوي و بيان به‌كارگيري در فرآيندكاوي

In recent years, machine learning– an‎d deep learning–based classification has become one of the core pillars of decision support systems in sensitive domains such as medicine, finance, industry, energy, an‎d environmental sciences. Despite remarkable advances in deep learning architectures, a review of the research literature indicates that many existing models, despite achieving high numerical accuracy, still face fundamental challenges such as learning instability, sensitivity to initialization an‎d ran‎dom seeds, an‎d a misalignment between continuous loss functions an‎d the discrete nature of classification problems. These challenges, particularly in safety-critical an‎d sensitive applications, cast doubt on the reliability an‎d practical deployability of such models. In this dissertation, a novel approach entitled Reliable Jump-Based Deep Learning is proposed, which, as a hybrid model, integrates discrete, direction-oriented learning logic with the concept of reliability in the training process. Within this framework, instead of continuous an‎d gradual gradient-based updat‎es, the learning process relies on discrete, directional jumps in the parameter space, an‎d only those updat‎es are accepted that, in addition to improving training performance, demonstrate adequate stability on validation data. This mechanism reduces the conceptual gap between continuous optimization an‎d the discrete objective of classification, thereby enhancing model stability an‎d reliability. To eva‎luate the performance of the proposed approach, it has been implemented on diverse datasets from various data mining domains, including medical, environmental, an‎d energy-related datasets, an‎d compared with classical methods an‎d conventional deep learning techniques. Experimental results show that the proposed model achieves an average accuracy improvement of approximately 6.65% over stan‎dard deep feedforward neural networks, an‎d in certain fault detection applications, classification accuracy increases from around 52% in classical methods to over 99%. Moreover, multi-seed experimental analysis indicates that the new approach significantly reduces performance variance an‎d exhibits more stable an‎d predictable behavior with respect to initialization an‎d ran‎dom fluctuations. Subsequently, the applicability of the proposed approach within the framework of process mining, particularly in the task of next-activity prediction, is investigated. The results indicate that although the full implementation of reliable jump-based deep learning at the scale of process mining involves challenges such as computational complexity an‎d time costs, improvements in classification models at the data mining level can directly enhance the accuracy an‎d efficiency of process analysis. Overall, by redefining the learning logic of classification an‎d presenting a hybrid framework based on reliable jumps, this dissertation takes a step toward the development of more accurate, stable, an‎d trustworthy models in data mining an‎d sensitive applications, an‎d provides a clear pathway for future research an‎d development in the fields of deep learning an‎d process mining.

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صبا صارمي نيا , ساره گلي فروشاني , علي بزرگي اميري