2 research outputs found
KNH: Multi-View Modeling with K-Nearest Hyperplanes Graph for Misinformation Detection
Graphs are one of the most efficacious structures for representing datapoints
and their relations, and they have been largely exploited for different
applications. Previously, the higher-order relations between the nodes have
been modeled by a generalization of graphs known as hypergraphs. In
hypergraphs, the edges are defined by a set of nodes i.e., hyperedges to
demonstrate the higher order relationships between the data. However, there is
no explicit higher-order generalization for nodes themselves. In this work, we
introduce a novel generalization of graphs i.e., K-Nearest Hyperplanes graph
(KNH) where the nodes are defined by higher order Euclidean subspaces for
multi-view modeling of the nodes. In fact, in KNH, nodes are hyperplanes or
more precisely m-flats instead of datapoints. We experimentally evaluate the
KNH graph on two multi-aspect datasets for misinformation detection. The
experimental results suggest that multi-view modeling of articles using KNH
graph outperforms the classic KNN graph in terms of classification performance
Binary Classification in Unstructured Space With Hypergraph Case-Based Reasoning
Binary classification is one of the most common problem in machine learning.
It consists in predicting whether a given element belongs to a particular
class. In this paper, a new algorithm for binary classification is proposed
using a hypergraph representation. The method is agnostic to data
representation, can work with multiple data sources or in non-metric spaces,
and accommodates with missing values. As a result, it drastically reduces the
need for data preprocessing or feature engineering. Each element to be
classified is partitioned according to its interactions with the training set.
For each class, a seminorm over the training set partition is learnt to
represent the distribution of evidence supporting this class.
Empirical validation demonstrates its high potential on a wide range of
well-known datasets and the results are compared to the state-of-the-art. The
time complexity is given and empirically validated. Its robustness with regard
to hyperparameter sensitivity is studied and compared to standard
classification methods. Finally, the limitation of the model space is
discussed, and some potential solutions proposed.Comment: Accepted for publication by Information Systems. Arxiv version
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