Arancha Simon-Hurtado

Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methods

Microarray data classification is a task involving high dimensionality and small samples sizes. A common criterion to decide on the number of selected genes is maximizing the accuracy, which risks overfitting and usually selects more genes than actually needed. We propose, relaxing the maximum accuracy criterion, to select the combination of attribute selection and classification algorithm that using less attributes has an accuracy not statistically significantly worst that the best. Also we give some advice to choose a suitable combination of attribute selection and classifying algorithms for a good accuracy when using a low number of gene expressions. We used some well known attribute selection methods (FCBF, ReliefF and SVM-RFE, plus a Random selection, used as a base line technique) and classifying techniques (Naive Bayes, 3 Nearest Neighbor and SVM with linear kernel) applied to 30 data sets involving different cancer types.

Improving ANN performance for imbalanced data sets by means of the NTIL technique

This paper deals with the problem of training an Artificial Neural Network (ANN) when the data sets are very imbalanced. Most learning algorithms, including ANN, are designed for well-balanced data and do not work properly on imbalanced ones. Of the approaches proposed for dealing with this problem, we are interested in the re-sampling ones, since they are algorithm-independent. We have recently proposed a new under-sampling technique for the two-class problem, called Non-Target Incremental Learning (NTIL), which has shown a good performance with SVM, improving results and training speed. Here, the advantages of using this technique with ANN are shown. The performance with regard to other popular under-sampling techniques is compared.

Client threshold prediction in biometric signature recognition by means of Multiple Linear Regression and its use for score normalization

Abstract Biometric person authentication has become an important area of fieldwork both for research and commercial purposes in the last few years. The development of the technology, now ready for practical applications, has encouraged the scientific community to focus on practical issues. In this sense, a key question is the decision threshold estimation. Biometric authentication is a pattern recognition problem where a final decision (identity accepted/rejected) must be taken; so, to set a correct decision threshold is essential, since the best system becomes useless if an inaccurate decision threshold is fixed. This work focuses on this subject for biometric systems based on manuscript signatures. The decision threshold can be client (signatory) dependent or the same for all (common threshold). In this paper, new approaches for both problems are shown. A new solution, based on the Multiple Linear Regression model, is proposed for client dependent decision threshold estimation or prediction. The state of the art shows that only independent variables based on the Gaussian scores distribution supposition have been used. Here, new robust parameters, not based on that supposition, have been successfully included in the model. This proposal has been evaluated by means of both a statistical validation and a performance comparison with the state of the art. When a common threshold is used, the problem is to normalize the client scores. A new proposal for this task is also shown, based on the use of the predicted client threshold. Both proposals have been multi-working point, multi-corpus and multi-classifier tested. Improvements from 12% to 57% have been achieved with respect to the state of the art in threshold prediction, while these improvements are from 15% to 40% in the score normalization task.

Learner-Adaptive Pedagogical Model in SIAL, an Open-Ended Intelligent Tutoring System for First Order Logic

SIAL is an Intelligent Tutoring System (ITS) for learning Computational Logic. It teaches classical refutation by resolution concepts using Robinson’s Binary Resolution Rule. Furthermore, SIAL can be considered a Model-Based System, as its Domain Model is an Automated Theorem Prover (ATP) for First Order Logic (FOL). This allows SIAL to accept several solutions to the proposed exercise, all of which are correct, providing a kind of open-ended feature to the ITS. The Domain Model is in charge of carrying out the error diagnosis, identifying, in many cases, the misunderstandings. The focus of this paper is to describe the Pedagogical Model of SIAL that takes advantage of the error diagnosis capabilities of the Domain Model to offer a learner-adaptive tutorial action, according to the user cognitive profile.

A new approach for a priori client threshold estimation in biometric signature recognition based on multiple linear regression

This paper presents a novel approach to estimate (predict) the a priori client decision threshold for biometric recognition systems based on multiple linear regression. Biometric recognition is a complex classification problem where the goal is to classify a pattern (biometric sample) as belonging or not to a certain class (client). As in other pattern recognition problems, a correct estimation of the decision threshold is essential for optimizing the biometric systems performance. Our proposal is tested in biometric signature recognition, estimating thresholds for different system working points. A theoretical and practical performance analysis is presented, including a comparison with the state of the art, showing the advantages, in system performance, of our proposal.

A New Proposal for Score Normalization in Biometric Signature Recognition Based on Client Threshold Prediction

Score Normalization is a usual technique in pattern recognition to standardize the classifier output ranges so as to, for example, fuse these outputs. The use of score normalization in biometric recognition is a very important part of the system, specially in those based on behavioral traits, such as written signature or voice, conditioning the final system performance. Then, many works can be found that focus on the problem. A successful new approach for client threshold prediction, based on Multiple Linear Prediction, has been presented in recent works. Here, a new approach for score normalization, based on this proposal for biometric manuscript signature user verification, is shown. This proposal is compared with the state of the art methods, achieving an improvement of 19% and 16% for Equal Error Rate (EER) and 60% and 26% for Detection Cost Function (DCF) performance measures, for random and skilled forgeries, respectively.

On the Use of Score Ratio with Distance-Based Classifiers in Biometric Signature Recognition

Biometric user verification or authentication is a pattern recognition problem that can be stated as a basic hypothesis test: X is from client C (\(H_0\)) vs. X is not from client C (\(H_1\)), where X is the biometric input sample (face, fingerprint, etc.). When probabilistic classifiers are used (e.g., Hidden Markov Models), the decision is typically performed by means of the likelihood ratio: \({P(X/H_0)}/{P(X/H_1)}\). However, as far as we know, this ratio is not usually performed when distance-based classifiers (e.g., Dynamic Time Warping) are used. Following that idea, we propose, here, to perform the decision based not only on the score (“score” being the classifier output) supposing X is from the client (\(H_0\)), but also using the score supposing X is not from the client (\(H_1\)), by means of the ratio between both scores: the score ratio. A first approach to this proposal can be seen in this work, showing that to use the score ratio can be an interesting technique to improve distance-based biometric systems. This research has focused on the biometric signature, where several state of the art systems based on distance can be found. Here, the score ratio proposal is tested in three of them, achieving great improvements in the majority of the tests performed. The best verification results have been achieved with the use of the score ratio, improving the best ones without the score ratio by, on average, 24 %.

Adding a graphical output to a theorem prover: Results of a comparison of the acceptance from a teaching point of view

This article describes the experience and results obtained from teaching computational logic in an introductory course for undergraduates. The proposed teaching approach helps students to reach a deeper level of understanding in first order logic representation, computational logic procedures and automated theorem proving. The article includes the description of SLI, a theorem prover with a graphical output and the results of a survey to assess the acceptance of the tool.

A New Methodology for Feature Selection Based on Machine Learning Methods Applied to Glaucoma

In this paper we present a new methodology based on machine learning methods that allows to select from the available features that define a problem, a subset with the most discriminant ones to outperform a classification. As an application, we have used it to select, from the attributes of the optic nerve obtained by Heidelberg Retina Tomograph II, the most informative ones to discriminate between glaucoma and non-glaucoma. Applying this methodology we have identified 7 attributes from the original 103 attributes, improving the ROC area a 2.38%. These attributes match to a large extent with the most informative ones according to the ophthalmologists experience in clinic as well as the literature.