2 research outputs found
Exploring Correlation between Labels to improve Multi-Label Classification
This paper attempts multi-label classification by extending the idea of
independent binary classification models for each output label, and exploring
how the inherent correlation between output labels can be used to improve
predictions. Logistic Regression, Naive Bayes, Random Forest, and SVM models
were constructed, with SVM giving the best results: an improvement of 12.9\%
over binary models was achieved for hold out cross validation by augmenting
with pairwise correlation probabilities of the labels
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
CNNs, RNNs, GCNs, and CapsNets have shown significant insights in
representation learning and are widely used in various text mining tasks such
as large-scale multi-label text classification. However, most existing deep
models for multi-label text classification consider either the non-consecutive
and long-distance semantics or the sequential semantics, but how to consider
them both coherently is less studied. In addition, most existing methods treat
output labels as independent methods, but ignore the hierarchical relations
among them, leading to useful semantic information loss. In this paper, we
propose a novel hierarchical taxonomy-aware and attentional graph capsule
recurrent CNNs framework for large-scale multi-label text classification.
Specifically, we first propose to model each document as a word order preserved
graph-of-words and normalize it as a corresponding words-matrix representation
which preserves both the non-consecutive, long-distance and local sequential
semantics. Then the words-matrix is input to the proposed attentional graph
capsule recurrent CNNs for more effectively learning the semantic features. To
leverage the hierarchical relations among the class labels, we propose a
hierarchical taxonomy embedding method to learn their representations, and
define a novel weighted margin loss by incorporating the label representation
similarity. Extensive evaluations on three datasets show that our model
significantly improves the performance of large-scale multi-label text
classification by comparing with state-of-the-art approaches