Priyadharshini M, Banu A Faritha, Sharma Bhisham, Chowdhury Subrata, Rabie Khaled, Shongwe Thokozani
Department of Computer Science Engineering, Nalla Malla Reddy Engineering College, Hyderabad 500088, Telangana, India.
Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore 631027, Tamil Nadu, India.
Sensors (Basel). 2023 Jul 31;23(15):6836. doi: 10.3390/s23156836.
In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from different classes when producing synthetic samples. As a result, there can be more class overlap and more noise. To avoid this problem, this work presented an innovative technique called Adaptive Synthetic Data-Based Multi-label Classification (ASDMLC). Adaptive Synthetic (ADASYN) sampling is a sampling strategy for learning from unbalanced data sets. ADASYN weights minority class instances by learning difficulty. For hard-to-learn minority class cases, synthetic data are created. Their numerical variables are normalized with the help of the Min-Max technique to standardize the magnitude of each variable's impact on the outcomes. The values of the attribute in this work are changed to a new range, from 0 to 1, using the normalization approach. To raise the accuracy of multi-label classification, Velocity-Equalized Particle Swarm Optimization (VPSO) is utilized for feature selection. In the proposed approach, to overcome the premature convergence problem, standard PSO has been improved by equalizing the velocity with each dimension of the problem. To expose the inherent label dependencies, the multi-label classification ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Probabilistic Neural Network (PNN), and Clustering-Based Decision tree methods will be processed based on an averaging method. The following criteria, including precision, recall, accuracy, and error rate, are used to assess performance. The suggested model's multi-label classification accuracy is 90.88%, better than previous techniques, which is PCT, HOMER, and ML-Forest is 65.57%, 70.66%, and 82.29%, respectively.
近年来，机器学习和计算机视觉在多标签分类的应用方面都有所发展。目前，SMOTE已被用于现有研究中的数据平衡，但SMOTE在生成合成样本时没有考虑到附近的示例可能来自不同的类别。因此，可能会出现更多的类重叠和更多的噪声。为了避免这个问题，这项工作提出了一种创新技术，称为基于自适应合成数据的多标签分类（ASDMLC）。自适应合成（ADASYN）采样是一种从不平衡数据集中学习的采样策略。ADASYN根据学习难度对少数类实例进行加权。对于难以学习的少数类情况，创建合成数据。它们的数值变量借助Min-Max技术进行归一化，以标准化每个变量对结果的影响程度。在这项工作中，使用归一化方法将属性值更改为从0到1的新范围。为了提高多标签分类的准确性，采用速度均衡粒子群优化（VPSO）进行特征选择。在所提出的方法中，为了克服早熟收敛问题，通过使速度与问题的每个维度相等来改进标准粒子群优化。为了揭示固有的标签依赖性，将基于平均方法处理自适应神经模糊推理系统（ANFIS）、概率神经网络（PNN）和基于聚类的决策树方法的多标签分类集成。使用以下标准（包括精度、召回率、准确率和错误率）来评估性能。所提出模型的多标签分类准确率为90.88%，优于先前的技术，即PCT、HOMER和ML-Forest，它们的准确率分别为65.57%、70.66%和82.29%。
