The inherent class imbalance within textual data poses a significant challenge for machine learning-based techniques,as the available data often fails to adequately represent all classes.This scarcity of instances can...The inherent class imbalance within textual data poses a significant challenge for machine learning-based techniques,as the available data often fails to adequately represent all classes.This scarcity of instances can make it even more challenging when there are overlapping regions within different classes.To address these limitations,this study introduces a refinement model for textual data classification with imbalanced datasets.The proposed approach,refined classification using overlap data with bagging and genetic algorithms(ReCO-BGA),aims to refine the classification predictions by creating a two-tier classification process.First,a bagging model is employed,incorporating three distinct classes:majority,minority,and an additional extracted class specifically for overlapping instances.Second,we propose to rectify the predicted overlap instances using a genetic-based oversampling technique.To evaluate the performance of ReCO-BGA,we conducted several experiments,focusing on two practical use cases:hate speech detection and sentiment analysis.The results demonstrated the effectiveness of the proposed method and showed that it outperforms state-of-the-art methods.展开更多
The guaranteed cost control challenge for discrete-time nonlinear systems that include time-varying delays is the central topic of this paper.We propose a novel synthesis of state feedback controllers to achieve asymp...The guaranteed cost control challenge for discrete-time nonlinear systems that include time-varying delays is the central topic of this paper.We propose a novel synthesis of state feedback controllers to achieve asymptotic stability and ensure a satisfactory level of performance in the closed-loop system.To address the time-varying parameters and control,we leverage the Takagi-Sugeno(TS)fuzzy formalism,the Lyapunov-Krasovskii functional(LKF)framework,and free-weighting matrices.Furthermore,we establish novel delay-dependent linear matrix inequalities(LMIs)that guarantee the stability of the closed-loop system.To illustrate the benefits of our approach and compare it with existing literature works,we provide numerical examples.These examples showcase the practical application and advantages of the suggested approach.展开更多
文摘The inherent class imbalance within textual data poses a significant challenge for machine learning-based techniques,as the available data often fails to adequately represent all classes.This scarcity of instances can make it even more challenging when there are overlapping regions within different classes.To address these limitations,this study introduces a refinement model for textual data classification with imbalanced datasets.The proposed approach,refined classification using overlap data with bagging and genetic algorithms(ReCO-BGA),aims to refine the classification predictions by creating a two-tier classification process.First,a bagging model is employed,incorporating three distinct classes:majority,minority,and an additional extracted class specifically for overlapping instances.Second,we propose to rectify the predicted overlap instances using a genetic-based oversampling technique.To evaluate the performance of ReCO-BGA,we conducted several experiments,focusing on two practical use cases:hate speech detection and sentiment analysis.The results demonstrated the effectiveness of the proposed method and showed that it outperforms state-of-the-art methods.
文摘The guaranteed cost control challenge for discrete-time nonlinear systems that include time-varying delays is the central topic of this paper.We propose a novel synthesis of state feedback controllers to achieve asymptotic stability and ensure a satisfactory level of performance in the closed-loop system.To address the time-varying parameters and control,we leverage the Takagi-Sugeno(TS)fuzzy formalism,the Lyapunov-Krasovskii functional(LKF)framework,and free-weighting matrices.Furthermore,we establish novel delay-dependent linear matrix inequalities(LMIs)that guarantee the stability of the closed-loop system.To illustrate the benefits of our approach and compare it with existing literature works,we provide numerical examples.These examples showcase the practical application and advantages of the suggested approach.
