7,839 research outputs found
Linear Time Feature Selection for Regularized Least-Squares
We propose a novel algorithm for greedy forward feature selection for
regularized least-squares (RLS) regression and classification, also known as
the least-squares support vector machine or ridge regression. The algorithm,
which we call greedy RLS, starts from the empty feature set, and on each
iteration adds the feature whose addition provides the best leave-one-out
cross-validation performance. Our method is considerably faster than the
previously proposed ones, since its time complexity is linear in the number of
training examples, the number of features in the original data set, and the
desired size of the set of selected features. Therefore, as a side effect we
obtain a new training algorithm for learning sparse linear RLS predictors which
can be used for large scale learning. This speed is possible due to matrix
calculus based short-cuts for leave-one-out and feature addition. We
experimentally demonstrate the scalability of our algorithm and its ability to
find good quality feature sets.Comment: 17 pages, 15 figure
Machine learning for automatic prediction of the quality of electrophysiological recordings
The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select āgoodā recordings for further analyses. This procedure is time-consuming and prone to selection biases. Here, we investigate replacing human decisions by a machine learning approach. We define 16 features, such as spike height and width, select the most informative ones using a wrapper method and train a classifier to reproduce the judgement of one of our expert electrophysiologists. Generalisation performance is then assessed on unseen data, classified by the same or by another expert. We observe that the learning machine can be equally, if not more, consistent in its judgements as individual experts amongst each other. Best performance is achieved for a limited number of informative features; the optimal feature set being different from one data set to another. With 80ā90% of correct judgements, the performance of the system is very promising within the data sets of each expert but judgments are less reliable when it is used across sets of recordings from different experts. We conclude that the proposed approach is relevant to the selection of electrophysiological recordings, provided parameters are adjusted to different types of experiments and to individual experimenters
Feature Selection via Binary Simultaneous Perturbation Stochastic Approximation
Feature selection (FS) has become an indispensable task in dealing with
today's highly complex pattern recognition problems with massive number of
features. In this study, we propose a new wrapper approach for FS based on
binary simultaneous perturbation stochastic approximation (BSPSA). This
pseudo-gradient descent stochastic algorithm starts with an initial feature
vector and moves toward the optimal feature vector via successive iterations.
In each iteration, the current feature vector's individual components are
perturbed simultaneously by random offsets from a qualified probability
distribution. We present computational experiments on datasets with numbers of
features ranging from a few dozens to thousands using three widely-used
classifiers as wrappers: nearest neighbor, decision tree, and linear support
vector machine. We compare our methodology against the full set of features as
well as a binary genetic algorithm and sequential FS methods using
cross-validated classification error rate and AUC as the performance criteria.
Our results indicate that features selected by BSPSA compare favorably to
alternative methods in general and BSPSA can yield superior feature sets for
datasets with tens of thousands of features by examining an extremely small
fraction of the solution space. We are not aware of any other wrapper FS
methods that are computationally feasible with good convergence properties for
such large datasets.Comment: This is the Istanbul Sehir University Technical Report
#SHR-ISE-2016.01. A short version of this report has been accepted for
publication at Pattern Recognition Letter
Ensembles of wrappers for automated feature selection in fish age classification
In feature selection, the most important features must be chosen so as to decrease the number thereof while retaining their discriminatory information. Within this context, a novel feature selection method based on an ensemble of wrappers is proposed and applied for automatically select features in fish age classification. The effectiveness of this procedure using an Atlantic cod database has been tested for different powerful statistical learning classifiers. The subsets based on few features selected, e.g. otolith weight and fish weight, are particularly noticeable given current biological findings and practices in fishery research and the classification results obtained with them outperforms those of previous studies in which a manual feature selection was performed.Peer ReviewedPostprint (author's final draft
Exploring EEG Features in Cross-Subject Emotion Recognition
Recognizing cross-subject emotions based on brain imaging data, e.g., EEG, has always been difficult due to the poor generalizability of features across subjects. Thus, systematically exploring the ability of different EEG features to identify emotional information across subjects is crucial. Prior related work has explored this question based only on one or two kinds of features, and different findings and conclusions have been presented. In this work, we aim at a more comprehensive investigation on this question with a wider range of feature types, including 18 kinds of linear and non-linear EEG features. The effectiveness of these features was examined on two publicly accessible datasets, namely, the dataset for emotion analysis using physiological signals (DEAP) and the SJTU emotion EEG dataset (SEED). We adopted the support vector machine (SVM) approach and the "leave-one-subject-out" verification strategy to evaluate recognition performance. Using automatic feature selection methods, the highest mean recognition accuracy of 59.06% (AUC = 0.605) on the DEAP dataset and of 83.33% (AUC = 0.904) on the SEED dataset were reached. Furthermore, using manually operated feature selection on the SEED dataset, we explored the importance of different EEG features in cross-subject emotion recognition from multiple perspectives, including different channels, brain regions, rhythms, and feature types. For example, we found that the Hjorth parameter of mobility in the beta rhythm achieved the best mean recognition accuracy compared to the other features. Through a pilot correlation analysis, we further examined the highly correlated features, for a better understanding of the implications hidden in those features that allow for differentiating cross-subject emotions. Various remarkable observations have been made. The results of this paper validate the possibility of exploring robust EEG features in cross-subject emotion recognition
Decision table for classifying point sources based on FIRST and 2MASS databases
With the availability of multiwavelength, multiscale and multiepoch
astronomical catalogues, the number of features to describe astronomical
objects has increases. The better features we select to classify objects, the
higher the classification accuracy is. In this paper, we have used data sets of
stars and quasars from near infrared band and radio band. Then best-first
search method was applied to select features. For the data with selected
features, the algorithm of decision table was implemented. The classification
accuracy is more than 95.9%. As a result, the feature selection method improves
the effectiveness and efficiency of the classification method. Moreover the
result shows that decision table is robust and effective for discrimination of
celestial objects and used for preselecting quasar candidates for large survey
projects.Comment: 10 pages. accepted by Advances in Space Researc
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The robust selection of predictive genes via a simple classifier
Identifying genes that direct the mechanism of a disease from expression data is extremely useful in understanding how that mechanism works.
This in turn may lead to better diagnoses and potentially can lead to a cure for that disease. This task becomes extremely challenging when the
data are characterised by only a small number of samples and a high number of dimensions, as it is often the case with gene expression data.
Motivated by this challenge, we present a general framework that focuses on simplicity and data perturbation. These are the keys for the robust
identification of the most predictive features in such data. Within this framework, we propose a simple selective naĀØıve Bayes classifier discovered using a global search technique, and combine it with data perturbation to
increase its robustness to small sample sizes.
An extensive validation of the method was carried out using two applied datasets from the field of microarrays and a simulated dataset, all
confounded by small sample sizes and high dimensionality. The method has been shown capable of identifying genes previously confirmed or associated with prostate cancer and viral infections
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