This work follows the approach of multi - label classification for non - intrusive load monitoring (NILM) . We modify the popu lar sparse representation based classification (SRC) approach (developed for single label classification) to solve multi - label classification problems. Results on benchmark REDD and Pecan Street dataset shows significant improvement over state - of - the - art t echniques with small volume of training data . N non - intrusive load monitoring (NILM) the technical goal is to estimate the power consumption of different appliances given the aggregate smart - meter readings  . The broader social objective is to feedback this information to the household so that they can reduce power consumption and thereby save energy.
Extreme multi-label text classification (XMTC) aims at tagging a document with most relevant labels from an extremely large-scale label set. It is a challenging problem especially for the tail labels because there are only few training documents to build classifier. This paper is motivated to better explore the semantic relationship between each document and extreme labels by taking advantage of both document content and label correlation. Our objective is to establish an explicit label-aware representation for each document with a hybrid attention deep neural network model(LAHA). LAHA consists of three parts. The first part adopts a multi-label self-attention mechanism to detect the contribution of each word to labels. The second part exploits the label structure and document content to determine the semantic connection between words and labels in a same latent space. An adaptive fusion strategy is designed in the third part to obtain the final label-aware document representation so that the essence of previous two parts can be sufficiently integrated. Extensive experiments have been conducted on six benchmark datasets by comparing with the state-of-the-art methods. The results show the superiority of our proposed LAHA method, especially on the tail labels.
Multi-label classification with many classes has recently drawn a lot of attention. Existing methods address this problem by performing linear label space transformation to reduce the dimension of label space, and then conducting independent regression for each reduced label dimension. These methods however do not capture nonlinear correlations of the multiple labels and may lead to significant information loss in the process of label space reduction. In this paper, we first propose to exploit kernel canonical correlation analysis (KCCA) to capture nonlinear label correlation information and perform nonlinear label space reduction. Then we develop a novel label space reduction method that explicitly combines linear and nonlinear label space transformations based on CCA and KCCA respectively to address multi-label classification with many classes. The proposed method is a feature-aware label transformation method that promotes the label predictability in the transformed label space from the input features. We conduct experiments on a number of multi-label classification datasets. The proposed approach demonstrates good performance, comparing to a number of state-of-the-art label dimension reduction methods.
To capture the interdependencies between labels in multi-label classification problems, classifier chain (CC) tries to take the multiple labels of each instance into account under a deterministic high-order Markov Chain model. Since its performance is sensitive to the choice of label order, the key issue is how to determine the optimal label order for CC. In this work, we first generalize the CC model over a random label order. Then, we present a theoretical analysis of the generalization error for the proposed generalized model. Based on our results, we propose a dynamic programming based classifier chain (CC-DP) algorithm to search the globally optimal label order for CC and a greedy classifier chain (CC-Greedy) algorithm to find a locally optimal CC.
Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received online and in chunks. Many of the current multi-label classification methods require a lot of time and memory, which make them infeasible for practical real-world applications. In this paper, we propose a fast linear label space dimension reduction method that transforms the labels into a reduced encoded space and trains models on the obtained pseudo labels. Additionally, it provides an analytical method to update the decoding matrix which maps the labels into the original space and is used during the test phase. Experimental results show the effectiveness of this approach in terms of running times and the prediction performance over different measures.