This paper proposed a novel feature selection method LUIFS （ latent utility of irrelevant feature selection） that not only selects the relevant features, but also targets at discovering the latent useful irrelevant ...This paper proposed a novel feature selection method LUIFS （ latent utility of irrelevant feature selection） that not only selects the relevant features, but also targets at discovering the latent useful irrelevant attributes by measuring their supportive importance to other attributes. The method minimizes the information lost and simultaneously maximizes the final classification accuracy. The classification error rates of the LUIFS method on 16 real-life datasets from UCI machine learning repository were evaluated using the ID3, Na^ve-Bayes, and IB （instance-based classifier） learning algorithms, respectively; and compared with those of the same algorithms with no feature selection （NoFS）, feature subset selection （FSS）, and correlation-based feature selection （CFS）. The empirical results demonstrate that the LUIFS can improve the performance of learning algorithms by taking the latent relevance for irrelevant attributes into consideration, and hence including those potentially important attributes into the optimal feature subset for classification.展开更多
Accurate classification of cassava disease,particularly in field scenarios,relies on object semantic localization to identify and precisely locate specific objects within an image based on their semantic meaning,there...Accurate classification of cassava disease,particularly in field scenarios,relies on object semantic localization to identify and precisely locate specific objects within an image based on their semantic meaning,thereby enabling targeted classification while suppressing rrelevant noise and focusing on key semantic features.The advancement of deep convolutional neural networks(CNNs)paved the way for identifying cassava diseases by leveraging salient semantic features and promising high returns.This study proposes an approach that incorporates three innovative elements to refine feature representation for cassava disease classification.First,a mutualattention method is introduced to highlight semantic features and suppress irrelevant background features in the feature maps.Second,instance batch normalization(IBN)was employed after the residual unit to construct salient semantic features using the mutualattention method,representing high-quality semantic features in the foreground.Finally,the RSigELUD activation method replaced the conventional ReLU activation,enhancing the nonlinear mapping capacity of the proposed neural network and further improving fine-grained leaf disease classification performance This approach significantly aided in distinguishing subtle disease manifestations in cassava leaves.The proposed neural network,MAIRNet-101(Mutualattention IBN RSigELUD Neural Network),achieved an accuracy of 95.30%and an F1-score of 0.9531,outperforming EfficientNet-B5 and RepVGG-B3g4.To evaluate the generalization capability of MAIRNet,the FGVC-Aircraft dataset was used to train MAIRNet-50,which achieved an accuracy of 83.64%.These results suggest that the proposed algorithm is well suited for cassava leaf disease classification applications and offers a robust solution for advancing agricultural technology.展开更多
基金The Science and Technology Development Fund from Macao Government (No007/2006/A)
文摘This paper proposed a novel feature selection method LUIFS （ latent utility of irrelevant feature selection） that not only selects the relevant features, but also targets at discovering the latent useful irrelevant attributes by measuring their supportive importance to other attributes. The method minimizes the information lost and simultaneously maximizes the final classification accuracy. The classification error rates of the LUIFS method on 16 real-life datasets from UCI machine learning repository were evaluated using the ID3, Na^ve-Bayes, and IB （instance-based classifier） learning algorithms, respectively; and compared with those of the same algorithms with no feature selection （NoFS）, feature subset selection （FSS）, and correlation-based feature selection （CFS）. The empirical results demonstrate that the LUIFS can improve the performance of learning algorithms by taking the latent relevance for irrelevant attributes into consideration, and hence including those potentially important attributes into the optimal feature subset for classification.
文摘Accurate classification of cassava disease,particularly in field scenarios,relies on object semantic localization to identify and precisely locate specific objects within an image based on their semantic meaning,thereby enabling targeted classification while suppressing rrelevant noise and focusing on key semantic features.The advancement of deep convolutional neural networks(CNNs)paved the way for identifying cassava diseases by leveraging salient semantic features and promising high returns.This study proposes an approach that incorporates three innovative elements to refine feature representation for cassava disease classification.First,a mutualattention method is introduced to highlight semantic features and suppress irrelevant background features in the feature maps.Second,instance batch normalization(IBN)was employed after the residual unit to construct salient semantic features using the mutualattention method,representing high-quality semantic features in the foreground.Finally,the RSigELUD activation method replaced the conventional ReLU activation,enhancing the nonlinear mapping capacity of the proposed neural network and further improving fine-grained leaf disease classification performance This approach significantly aided in distinguishing subtle disease manifestations in cassava leaves.The proposed neural network,MAIRNet-101(Mutualattention IBN RSigELUD Neural Network),achieved an accuracy of 95.30%and an F1-score of 0.9531,outperforming EfficientNet-B5 and RepVGG-B3g4.To evaluate the generalization capability of MAIRNet,the FGVC-Aircraft dataset was used to train MAIRNet-50,which achieved an accuracy of 83.64%.These results suggest that the proposed algorithm is well suited for cassava leaf disease classification applications and offers a robust solution for advancing agricultural technology.
