Feature relevance in dermoscopy images by the use of ABCD standard

Feature space heterogeneity often exists in many real world data sets so that some features are of different importance for classification over different subsets. Moreover, the pattern of feature space heterogeneity might dynamically change over time as more and more data are accumulated. In this paper, we develop an incremental classification algorithm, Supervised Clustering for Classification with Feature Space Heterogeneity (SCCFSH), to address this problem. In our approach, supervised clustering is implemented to obtain a number of clusters such that samples in each cluster are from the same class. After the removal of outliers, relevance of features in each cluster is calculated based on their variations in this cluster. The feature relevance is incorporated into distance calculation for classification. The main advantage of SCCFSH lies in the fact that it is capable of solving a classification problem with feature space heterogeneity in an incremental way, which is favorable for online classification tasks with continuously changing data. Experimental results on a series of data sets and application to a database marketing problem show the efficiency and effectiveness of the proposed approach.

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Active Learning of Feature Relevance

Computational Methods of Feature Selection - Chapman & Hall/CRC Data Mining and Knowledge Discovery Series ◽

10.1201/9781584888796.pt2 ◽

2007 ◽

pp. 87-107

Author(s):

Emanuele Olivetti ◽

Sriharsha Veeramachaneni ◽

Paolo Avesani

Keyword(s):

Active Learning ◽

Feature Relevance

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FEATURE SELECTION AND THE CHESSBOARD PROBLEM

Acta Universitatis Lodziensis Folia oeconomica ◽

10.18778/0208-6018.311.03 ◽

2015 ◽

Vol 1 (311) ◽

Author(s):

Mariusz Kubus

Keyword(s):

Feature Selection ◽

Data Structure ◽

Important Criterion ◽

Multivariate Approach ◽

Generalization Error ◽

Selection Methods ◽

Embedded Methods ◽

Feature Relevance

Feature selection methods are usually classified into three groups: filters, wrappers and embedded methods. The second important criterion of their classification is an individual or multivariate approach to evaluation of the feature relevance. The chessboard problem is an illustrative example, where two variables which have no individual influence on the dependent variable can be essential to separate the classes. The classifiers which deal well with such data structure are sensitive to irrelevant variables. The generalization error increases with the number of noisy variables. We discuss the feature selection methods in the context of chessboard-like structure in the data with numerous irrelevant variables.

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EOG feature relevance determination for microsleep detection

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2017-0172 ◽

2017 ◽

Vol 3 (2) ◽

pp. 815-818

Author(s):

Martin Golz ◽

Sebastian Wollner ◽

David Sommer ◽

Sebastian Schnieder

Keyword(s):

Cross Correlation ◽

Feature Reduction ◽

Support Vector ◽

Spectral Bands ◽

Automatic Relevance Determination ◽

Signal Features ◽

Power Spectral ◽

Vector Machines ◽

Power Spectral Densities ◽

Feature Relevance

AbstractAutomatic relevance determination (ARD) was applied to two-channel EOG recordings for microsleep event (MSE) recognition. 10 s immediately before MSE and also before counterexamples of fatigued, but attentive driving were analysed. Two type of signal features were extracted: the maximum cross correlation (MaxCC) and logarithmic power spectral densities (PSD) averaged in spectral bands of 0.5 Hz width ranging between 0 and 8 Hz. Generalised learn-ing vector quantisation (GRLVQ) was used as ARD method to show the potential of feature reduction. This is compared to support-vector machines (SVM), in which the feature reduction plays a much smaller role. Cross validation yielded mean normalised relevancies of PSD features in the range of 1.6 - 4.9 % and 1.9 - 10.4 % for horizontal and vertical EOG, respectively. MaxCC relevancies were 0.002 - 0.006 % and 0.002 - 0.06 %, respectively. This shows that PSD features of vertical EOG are indispensable, whereas MaxCC can be neglected. Mean classification accuracies were estimated at 86.6±b 1.3 % and 92.3±b 0.2 % for GRLVQ and SVM, respec-tively. GRLVQ permits objective feature reduction by inclu-sion of all processing stages, but is not as accurate as SVM.

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The Effect of Evidence Transfer on Latent Feature Relevance for Clustering

Informatics ◽

10.3390/informatics6020017 ◽

2019 ◽

Vol 6 (2) ◽

pp. 17

Author(s):

Athanasios Davvetas ◽

Iraklis A. Klampanos ◽

Spiros Skiadopoulos ◽

Vangelis Karkaletsis

Keyword(s):

Mutual Information ◽

Ground Truth ◽

Original Data ◽

Information Theoretic ◽

Information Bottleneck ◽

Latent Space ◽

Before And After ◽

Feature Relevance ◽

Latent Representations ◽

Transfer Method

Evidence transfer for clustering is a deep learning method that manipulates the latent representations of an autoencoder according to external categorical evidence with the effect of improving a clustering outcome. Evidence transfer’s application on clustering is designed to be robust when introduced with a low quality of evidence, while increasing the effectiveness of the clustering accuracy during relevant corresponding evidence. We interpret the effects of evidence transfer on the latent representation of an autoencoder by comparing our method to the information bottleneck method. Information bottleneck is an optimisation problem of finding the best tradeoff between maximising the mutual information of data representations and a task outcome while at the same time being effective in compressing the original data source. We posit that the evidence transfer method has essentially the same objective regarding the latent representations produced by an autoencoder. We verify our hypothesis using information theoretic metrics from feature selection in order to perform an empirical analysis over the information that is carried through the bottleneck of the latent space. We use the relevance metric to compare the overall mutual information between the latent representations and the ground truth labels before and after their incremental manipulation, as well as, to study the effects of evidence transfer regarding the significance of each latent feature.

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