13,563 research outputs found
Large scale biomedical texts classification: a kNN and an ESA-based approaches
With the large and increasing volume of textual data, automated methods for
identifying significant topics to classify textual documents have received a
growing interest. While many efforts have been made in this direction, it still
remains a real challenge. Moreover, the issue is even more complex as full
texts are not always freely available. Then, using only partial information to
annotate these documents is promising but remains a very ambitious issue.
MethodsWe propose two classification methods: a k-nearest neighbours
(kNN)-based approach and an explicit semantic analysis (ESA)-based approach.
Although the kNN-based approach is widely used in text classification, it needs
to be improved to perform well in this specific classification problem which
deals with partial information. Compared to existing kNN-based methods, our
method uses classical Machine Learning (ML) algorithms for ranking the labels.
Additional features are also investigated in order to improve the classifiers'
performance. In addition, the combination of several learning algorithms with
various techniques for fixing the number of relevant topics is performed. On
the other hand, ESA seems promising for this classification task as it yielded
interesting results in related issues, such as semantic relatedness computation
between texts and text classification. Unlike existing works, which use ESA for
enriching the bag-of-words approach with additional knowledge-based features,
our ESA-based method builds a standalone classifier. Furthermore, we
investigate if the results of this method could be useful as a complementary
feature of our kNN-based approach.ResultsExperimental evaluations performed on
large standard annotated datasets, provided by the BioASQ organizers, show that
the kNN-based method with the Random Forest learning algorithm achieves good
performances compared with the current state-of-the-art methods, reaching a
competitive f-measure of 0.55% while the ESA-based approach surprisingly
yielded reserved results.ConclusionsWe have proposed simple classification
methods suitable to annotate textual documents using only partial information.
They are therefore adequate for large multi-label classification and
particularly in the biomedical domain. Thus, our work contributes to the
extraction of relevant information from unstructured documents in order to
facilitate their automated processing. Consequently, it could be used for
various purposes, including document indexing, information retrieval, etc.Comment: Journal of Biomedical Semantics, BioMed Central, 201
A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear Manifold Distributed Data Classification
Nearest Neighbors (NN) is one of the most widely used supervised
learning algorithms to classify Gaussian distributed data, but it does not
achieve good results when it is applied to nonlinear manifold distributed data,
especially when a very limited amount of labeled samples are available. In this
paper, we propose a new graph-based NN algorithm which can effectively
handle both Gaussian distributed data and nonlinear manifold distributed data.
To achieve this goal, we first propose a constrained Tired Random Walk (TRW) by
constructing an -level nearest-neighbor strengthened tree over the graph,
and then compute a TRW matrix for similarity measurement purposes. After this,
the nearest neighbors are identified according to the TRW matrix and the class
label of a query point is determined by the sum of all the TRW weights of its
nearest neighbors. To deal with online situations, we also propose a new
algorithm to handle sequential samples based a local neighborhood
reconstruction. Comparison experiments are conducted on both synthetic data
sets and real-world data sets to demonstrate the validity of the proposed new
NN algorithm and its improvements to other version of NN algorithms.
Given the widespread appearance of manifold structures in real-world problems
and the popularity of the traditional NN algorithm, the proposed manifold
version NN shows promising potential for classifying manifold-distributed
data.Comment: 32 pages, 12 figures, 7 table
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