22,067 research outputs found
Embedding Feature Selection for Large-scale Hierarchical Classification
Large-scale Hierarchical Classification (HC) involves datasets consisting of
thousands of classes and millions of training instances with high-dimensional
features posing several big data challenges. Feature selection that aims to
select the subset of discriminant features is an effective strategy to deal
with large-scale HC problem. It speeds up the training process, reduces the
prediction time and minimizes the memory requirements by compressing the total
size of learned model weight vectors. Majority of the studies have also shown
feature selection to be competent and successful in improving the
classification accuracy by removing irrelevant features. In this work, we
investigate various filter-based feature selection methods for dimensionality
reduction to solve the large-scale HC problem. Our experimental evaluation on
text and image datasets with varying distribution of features, classes and
instances shows upto 3x order of speed-up on massive datasets and upto 45% less
memory requirements for storing the weight vectors of learned model without any
significant loss (improvement for some datasets) in the classification
accuracy. Source Code: https://cs.gmu.edu/~mlbio/featureselection.Comment: IEEE International Conference on Big Data (IEEE BigData 2016
Machine Learning in Automated Text Categorization
The automated categorization (or classification) of texts into predefined
categories has witnessed a booming interest in the last ten years, due to the
increased availability of documents in digital form and the ensuing need to
organize them. In the research community the dominant approach to this problem
is based on machine learning techniques: a general inductive process
automatically builds a classifier by learning, from a set of preclassified
documents, the characteristics of the categories. The advantages of this
approach over the knowledge engineering approach (consisting in the manual
definition of a classifier by domain experts) are a very good effectiveness,
considerable savings in terms of expert manpower, and straightforward
portability to different domains. This survey discusses the main approaches to
text categorization that fall within the machine learning paradigm. We will
discuss in detail issues pertaining to three different problems, namely
document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey
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