12,655 research outputs found
Impact of Biases in Big Data
The underlying paradigm of big data-driven machine learning reflects the
desire of deriving better conclusions from simply analyzing more data, without
the necessity of looking at theory and models. Is having simply more data
always helpful? In 1936, The Literary Digest collected 2.3M filled in
questionnaires to predict the outcome of that year's US presidential election.
The outcome of this big data prediction proved to be entirely wrong, whereas
George Gallup only needed 3K handpicked people to make an accurate prediction.
Generally, biases occur in machine learning whenever the distributions of
training set and test set are different. In this work, we provide a review of
different sorts of biases in (big) data sets in machine learning. We provide
definitions and discussions of the most commonly appearing biases in machine
learning: class imbalance and covariate shift. We also show how these biases
can be quantified and corrected. This work is an introductory text for both
researchers and practitioners to become more aware of this topic and thus to
derive more reliable models for their learning problems
Large-Scale Online Semantic Indexing of Biomedical Articles via an Ensemble of Multi-Label Classification Models
Background: In this paper we present the approaches and methods employed in
order to deal with a large scale multi-label semantic indexing task of
biomedical papers. This work was mainly implemented within the context of the
BioASQ challenge of 2014. Methods: The main contribution of this work is a
multi-label ensemble method that incorporates a McNemar statistical
significance test in order to validate the combination of the constituent
machine learning algorithms. Some secondary contributions include a study on
the temporal aspects of the BioASQ corpus (observations apply also to the
BioASQ's super-set, the PubMed articles collection) and the proper adaptation
of the algorithms used to deal with this challenging classification task.
Results: The ensemble method we developed is compared to other approaches in
experimental scenarios with subsets of the BioASQ corpus giving positive
results. During the BioASQ 2014 challenge we obtained the first place during
the first batch and the third in the two following batches. Our success in the
BioASQ challenge proved that a fully automated machine-learning approach, which
does not implement any heuristics and rule-based approaches, can be highly
competitive and outperform other approaches in similar challenging contexts
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