23,495 research outputs found
Building Gene Expression Profile Classifiers with a Simple and Efficient Rejection Option in R
Background: The collection of gene expression profiles from DNA microarrays and their analysis with pattern recognition algorithms is a powerful technology applied to several biological problems. Common pattern recognition systems classify samples assigning them to a set of known classes. However, in a clinical diagnostics setup, novel and unknown classes (new pathologies) may appear and one must be able to reject those samples that do not fit the trained model. The problem of implementing a rejection option in a multi-class classifier has not been widely addressed in the statistical literature. Gene expression profiles represent a critical case study since they suffer from the curse of dimensionality problem that negatively reflects on the reliability of both traditional rejection models and also more recent approaches such as one-class classifiers. Results: This paper presents a set of empirical decision rules that can be used to implement a rejection option in a set of multi-class classifiers widely used for the analysis of gene expression profiles. In particular, we focus on the classifiers implemented in the R Language and Environment for Statistical Computing (R for short in the remaining of this paper). The main contribution of the proposed rules is their simplicity, which enables an easy integration with available data analysis environments. Since in the definition of a rejection model tuning of the involved parameters is often a complex and delicate task, in this paper we exploit an evolutionary strategy to automate this process. This allows the final user to maximize the rejection accuracy with minimum manual intervention. Conclusions: This paper shows how the use of simple decision rules can be used to help the use of complex machine learning algorithms in real experimental setups. The proposed approach is almost completely automated and therefore a good candidate for being integrated in data analysis flows in labs where the machine learning expertise required to tune traditional classifiers might not be availabl
Optical Font Recognition in Smartphone-Captured Images, and its Applicability for ID Forgery Detection
In this paper, we consider the problem of detecting counterfeit identity
documents in images captured with smartphones. As the number of documents
contain special fonts, we study the applicability of convolutional neural
networks (CNNs) for detection of the conformance of the fonts used with the
ones, corresponding to the government standards. Here, we use multi-task
learning to differentiate samples by both fonts and characters and compare the
resulting classifier with its analogue trained for binary font classification.
We train neural networks for authenticity estimation of the fonts used in
machine-readable zones and ID numbers of the Russian national passport and test
them on samples of individual characters acquired from 3238 images of the
Russian national passport. Our results show that the usage of multi-task
learning increases sensitivity and specificity of the classifier. Moreover, the
resulting CNNs demonstrate high generalization ability as they correctly
classify fonts which were not present in the training set. We conclude that the
proposed method is sufficient for authentication of the fonts and can be used
as a part of the forgery detection system for images acquired with a smartphone
camera
A pragmatic approach to multi-class classification
We present a novel hierarchical approach to multi-class classification which
is generic in that it can be applied to different classification models (e.g.,
support vector machines, perceptrons), and makes no explicit assumptions about
the probabilistic structure of the problem as it is usually done in multi-class
classification. By adding a cascade of additional classifiers, each of which
receives the previous classifier's output in addition to regular input data,
the approach harnesses unused information that manifests itself in the form of,
e.g., correlations between predicted classes. Using multilayer perceptrons as a
classification model, we demonstrate the validity of this approach by testing
it on a complex ten-class 3D gesture recognition task.Comment: European Symposium on artificial neural networks (ESANN), Apr 2015,
Bruges, Belgium. 201
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