Hierarchical Multi-label Classification
11 papers with code • 16 benchmarks • 9 datasets
Multi-label classification is a standard machine learning problem in which an object can be associated with multiple labels. A hierarchical multi-label classification (HMC) problem is defined as a multi-label classification problem in which classes are hierarchically organized as a tree or as a directed acyclic graph (DAG), and in which every prediction must be coherent, i.e., respect the hierarchy constraint. The hierarchy constraint states that a datapoint belonging to a given class must also belong to all its ancestors in the hierarchy.
The main reason is that the tree-likeness of the hyperbolic space matches the complexity of symbolic data with hierarchical structures.
Also, learning of PCTs can not exploit the sparsity of data to improve the computational efficiency, which is common in both input (molecular fingerprints, bag of words representations) and output spaces (in multi-label classification, examples are often labeled with only a fraction of possible labels).
Hierarchical multi-label classification (HMC) is a challenging classification task extending standard multi-label classification problems by imposing a hierarchy constraint on the classes.
Such a joint learning is expected to provide a twofold advantage: i) the classifier generalizes better as it leverages the prior knowledge of existence of a hierarchy over the labels, and ii) in addition to the label co-occurrence information, the label-embedding may benefit from the manifold structure of the input datapoints, leading to embeddings that are more faithful to the label hierarchy.
Multi-label classification (MC) is a standard machine learning problem in which a data point can be associated with a set of classes.
We provide theoretical grounding for our method and show experimentally the model's ability to learn the true latent taxonomic structure from data.
Feature extraction using Spectral Clustering for Gene Function Prediction using Hierarchical Multi-label Classification
Gene annotation addresses the problem of predicting unknown associations between gene and functions (e. g., biological processes) of a specific organism.
We design a predictive layer for structured-output prediction (SOP) that can be plugged into any neural network guaranteeing its predictions are consistent with a set of predefined symbolic constraints.