81,246 research outputs found
Human activity recognition making use of long short-term memory techniques
The optimisation and validation of a classifiers performance when applied to real
world problems is not always effectively shown. In much of the literature describing
the application of artificial neural network architectures to Human Activity
Recognition (HAR) problems, postural transitions are grouped together and treated as
a singular class. This paper proposes, investigates and validates the development of
an optimised artificial neural network based on Long-Short Term Memory techniques
(LSTM), with repeated cross validation used to validate the performance of the
classifier. The results of the optimised LSTM classifier are comparable or better to
that of previous research making use of the same dataset, achieving 95% accuracy
under repeated 10-fold cross validation using grouped postural transitions. The work
in this paper also achieves 94% accuracy under repeated 10-fold cross validation
whilst treating each common postural transition as a separate class (and thus
providing more context to each activity)
Magnetic and radar sensing for multimodal remote health monitoring
With the increased life expectancy and rise in health conditions related to aging, there is a need for new technologies that can routinely monitor vulnerable people, identify their daily pattern of activities and any anomaly or critical events such as falls. This paper aims to evaluate magnetic and radar sensors as suitable technologies for remote health monitoring purpose, both individually and fusing their information. After experiments and collecting data from 20 volunteers, numerical features has been extracted in both time and frequency domains. In order to analyse and verify the validation of fusion method for different classifiers, a Support Vector Machine with a quadratic kernel, and an Artificial Neural Network with one and multiple hidden layers have been implemented. Furthermore, for both classifiers, feature selection has been performed to obtain salient features. Using this technique along with fusion, both classifiers can detect 10 different activities with an accuracy rate of approximately 96%. In cases where the user is unknown to the classifier, an accuracy of approximately 92% is maintained
Pattern classification of cotton yarn neps
In this study, two types of cotton yarn neps, viz. seed coat and fibrous neps, have been classified by means of two standard classifiers, namely support vector machine and probabilistic neural network using the features extracted from the images of neps. At first, the region of interest is located in the captured images using k-means clustering algorithm, from which six features are extracted. These extracted features are used as dataset (both training and testing) for classifiers. A K-fold cross validation technique has been applied to assess the performance of the two classifiers. The results show that the neps classification accomplished by means of image recognition through these classifiers achieves nearly 96-97% accuracy for the test data set. Experimental results show that the required time for training probabilistic neural network is significantly less as compared to that of support vector machine
Combining Neuro-Fuzzy Classifiers for Improved Generalisation and Reliability
In this paper a combination of neuro-fuzzy
classifiers for improved classification performance and reliability
is considered. A general fuzzy min-max (GFMM) classifier with
agglomerative learning algorithm is used as a main building
block. An alternative approach to combining individual classifier
decisions involving the combination at the classifier model level is
proposed. The resulting classifier complexity and transparency is
comparable with classifiers generated during a single crossvalidation
procedure while the improved classification
performance and reduced variance is comparable to the ensemble
of classifiers with combined (averaged/voted) decisions. We also
illustrate how combining at the model level can be used for
speeding up the training of GFMM classifiers for large data sets
A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer
Recently, several classifiers that combine primary tumor data, like gene
expression data, and secondary data sources, such as protein-protein
interaction networks, have been proposed for predicting outcome in breast
cancer. In these approaches, new composite features are typically constructed
by aggregating the expression levels of several genes. The secondary data
sources are employed to guide this aggregation. Although many studies claim
that these approaches improve classification performance over single gene
classifiers, the gain in performance is difficult to assess. This stems mainly
from the fact that different breast cancer data sets and validation procedures
are employed to assess the performance. Here we address these issues by
employing a large cohort of six breast cancer data sets as benchmark set and by
performing an unbiased evaluation of the classification accuracies of the
different approaches. Contrary to previous claims, we find that composite
feature classifiers do not outperform simple single gene classifiers. We
investigate the effect of (1) the number of selected features; (2) the specific
gene set from which features are selected; (3) the size of the training set and
(4) the heterogeneity of the data set on the performance of composite feature
and single gene classifiers. Strikingly, we find that randomization of
secondary data sources, which destroys all biological information in these
sources, does not result in a deterioration in performance of composite feature
classifiers. Finally, we show that when a proper correction for gene set size
is performed, the stability of single gene sets is similar to the stability of
composite feature sets. Based on these results there is currently no reason to
prefer prognostic classifiers based on composite features over single gene
classifiers for predicting outcome in breast cancer
Learning Hybrid Neuro-Fuzzy Classifier Models From Data: To Combine or Not to Combine?
To combine or not to combine? Though not a question of the same gravity as the Shakespeare’s to be or not
to be, it is examined in this paper in the context of a hybrid neuro-fuzzy pattern classifier design process. A general fuzzy
min-max neural network with its basic learning procedure is used within six different algorithm independent learning
schemes. Various versions of cross-validation, resampling techniques and data editing approaches, leading to a generation
of a single classifier or a multiple classifier system, are scrutinised and compared. The classification performance on
unseen data, commonly used as a criterion for comparing different competing designs, is augmented by further four
criteria attempting to capture various additional characteristics of classifier generation schemes. These include: the ability
to estimate the true classification error rate, the classifier transparency, the computational complexity of the learning
scheme and the potential for adaptation to changing environments and new classes of data. One of the main questions
examined is whether and when to use a single classifier or a combination of a number of component classifiers within a
multiple classifier system
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