This paper presents a framework for multi-label scene classification, where a scene may contain multiple objects and be described by multiple class labels. Traditional pattern recognition assumes mutually exclusive classes, but in multi-label classification, classes may overlap. The authors propose a method to handle this scenario, using cross-training to build classifiers and introducing new metrics for evaluating classification performance. They demonstrate that their approach is effective for scene classification, even with limited training data, and that it generalizes to other classification tasks. The paper discusses different testing criteria, including the C-Criterion, which uses a threshold selected by the MAP principle, and the α-Evaluation, a novel metric that allows for flexible error forgiveness. The authors also compare the performance of different training models, showing that cross-training outperforms other methods. The results show that their approach achieves high accuracy in both single-label and multi-label classification tasks. The study highlights the importance of considering overlapping classes in scene classification and provides a framework for handling such cases. The authors also discuss the limitations of their approach and suggest future research directions.This paper presents a framework for multi-label scene classification, where a scene may contain multiple objects and be described by multiple class labels. Traditional pattern recognition assumes mutually exclusive classes, but in multi-label classification, classes may overlap. The authors propose a method to handle this scenario, using cross-training to build classifiers and introducing new metrics for evaluating classification performance. They demonstrate that their approach is effective for scene classification, even with limited training data, and that it generalizes to other classification tasks. The paper discusses different testing criteria, including the C-Criterion, which uses a threshold selected by the MAP principle, and the α-Evaluation, a novel metric that allows for flexible error forgiveness. The authors also compare the performance of different training models, showing that cross-training outperforms other methods. The results show that their approach achieves high accuracy in both single-label and multi-label classification tasks. The study highlights the importance of considering overlapping classes in scene classification and provides a framework for handling such cases. The authors also discuss the limitations of their approach and suggest future research directions.