Multi-label classification algorithms deal with classification problems where a single datapoint can be classified (or labelled) with more than one class (or label) at the same time. Early multi-label approaches like binary relevance consider each label individually and train individual binary classifier models for each label. State-of-the-art algorithms like RAkEL, classifier chains, calibrated label ranking, IBLR-ML+, and BPMLL also consider the associations between labels for improved performance. Like most machine learning algorithms, however, these approaches require careful hyper-parameter tuning, a computationally expensive optimisation problem. There is a scarcity of multi-label classification algorithms that require minimal hyper-parameter tuning. This paper addresses this gap in the literature by proposing CascadeML, a multi-label classification method based on the existing cascaded neural network architecture, which also takes label associations into consideration. CascadeML grows a neural network architecture incrementally (deep as well as wide) in a two-phase process as it learns network weights using an adaptive first-order gradient descent algorithm. This omits the requirement of preselecting the number of hidden layers, nodes, activation functions, and learning rate. The performance of the CascadeML algorithm was evaluated using 13 multi-label datasets and compared with nine existing multi-label algorithms. The results show that CascadeML achieved the best average rank over the datasets, performed better than BPMLL (one of the earliest well known multi-label specific neural network algorithms), and was similar to the state-of-the-art classifier chains and RAkEL algorithms.

中文翻译：

---

一种用于深度级联架构自动训练和进化的多标签级联神经网络分类算法

多标签分类算法处理分类问题，其中单个数据点可以同时被多个类（或标签）分类（或标记）。早期的多标签方法（如二元相关性）单独考虑每个标签，并为每个标签训练单独的二元分类器模型。RAkEL、分类器链、校准标签排名、IBLR-ML+ 和 BPMLL 等最先进的算法也考虑了标签之间的关联以提高性能。然而，与大多数机器学习算法一样，这些方法需要仔细的超参数调整，这是一个计算成本高的优化问题。需要最少超参数调整的多标签分类算法很少。本文通过提出 CascadeML 解决了文献中的这一空白，一种基于现有级联神经网络架构的多标签分类方法，它也考虑了标签关联。当 CascadeML 使用自适应一阶梯度下降算法学习网络权重时，CascadeML 在两阶段过程中逐步（深度和广度）增长神经网络架构。这省略了预选隐藏层数、节点数、激活函数和学习率的要求。使用 13 个多标签数据集评估 CascadeML 算法的性能，并与 9 个现有的多标签算法进行比较。结果表明，CascadeML 在数据集上取得了最好的平均排名，表现优于 BPMLL（最早的知名多标签特定神经网络算法之一），