20,798 research outputs found
Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
Multitask Learning is a learning paradigm that deals with multiple different
tasks in parallel and transfers knowledge among them. XOF, a Learning
Classifier System using tree-based programs to encode building blocks
(meta-features), constructs and collects features with rich discriminative
information for classification tasks in an observed list. This paper seeks to
facilitate the automation of feature transferring in between tasks by utilising
the observed list. We hypothesise that the best discriminative features of a
classification task carry its characteristics. Therefore, the relatedness
between any two tasks can be estimated by comparing their most appropriate
patterns. We propose a multiple-XOF system, called mXOF, that can dynamically
adapt feature transfer among XOFs. This system utilises the observed list to
estimate the task relatedness. This method enables the automation of
transferring features. In terms of knowledge discovery, the resemblance
estimation provides insightful relations among multiple data. We experimented
mXOF on various scenarios, e.g. representative Hierarchical Boolean problems,
classification of distinct classes in the UCI Zoo dataset, and unrelated tasks,
to validate its abilities of automatic knowledge-transfer and estimating task
relatedness. Results show that mXOF can estimate the relatedness reasonably
between multiple tasks to aid the learning performance with the dynamic feature
transferring.Comment: accepted by The Genetic and Evolutionary Computation Conference
(GECCO 2020
CIFAR-10: KNN-based Ensemble of Classifiers
In this paper, we study the performance of different classifiers on the
CIFAR-10 dataset, and build an ensemble of classifiers to reach a better
performance. We show that, on CIFAR-10, K-Nearest Neighbors (KNN) and
Convolutional Neural Network (CNN), on some classes, are mutually exclusive,
thus yield in higher accuracy when combined. We reduce KNN overfitting using
Principal Component Analysis (PCA), and ensemble it with a CNN to increase its
accuracy. Our approach improves our best CNN model from 93.33% to 94.03%
Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification
Objective. The main goal of this work is to develop a model for multi-sensor
signals such as MEG or EEG signals, that accounts for the inter-trial
variability, suitable for corresponding binary classification problems. An
important constraint is that the model be simple enough to handle small size
and unbalanced datasets, as often encountered in BCI type experiments.
Approach. The method involves linear mixed effects statistical model, wavelet
transform and spatial filtering, and aims at the characterization of localized
discriminant features in multi-sensor signals. After discrete wavelet transform
and spatial filtering, a projection onto the relevant wavelet and spatial
channels subspaces is used for dimension reduction. The projected signals are
then decomposed as the sum of a signal of interest (i.e. discriminant) and
background noise, using a very simple Gaussian linear mixed model. Main
results. Thanks to the simplicity of the model, the corresponding parameter
estimation problem is simplified. Robust estimates of class-covariance matrices
are obtained from small sample sizes and an effective Bayes plug-in classifier
is derived. The approach is applied to the detection of error potentials in
multichannel EEG data, in a very unbalanced situation (detection of rare
events). Classification results prove the relevance of the proposed approach in
such a context. Significance. The combination of linear mixed model, wavelet
transform and spatial filtering for EEG classification is, to the best of our
knowledge, an original approach, which is proven to be effective. This paper
improves on earlier results on similar problems, and the three main ingredients
all play an important role
Multi-TGDR: a regularization method for multi-class classification in microarray experiments
Background
With microarray technology becoming mature and popular, the selection and use
of a small number of relevant genes for accurate classification of samples is a
hot topic in the circles of biostatistics and bioinformatics. However, most of
the developed algorithms lack the ability to handle multiple classes, which
arguably a common application. Here, we propose an extension to an existing
regularization algorithm called Threshold Gradient Descent Regularization
(TGDR) to specifically tackle multi-class classification of microarray data.
When there are several microarray experiments addressing the same/similar
objectives, one option is to use meta-analysis version of TGDR (Meta-TGDR),
which considers the classification task as combination of classifiers with the
same structure/model while allowing the parameters to vary across studies.
However, the original Meta-TGDR extension did not offer a solution to the
prediction on independent samples. Here, we propose an explicit method to
estimate the overall coefficients of the biomarkers selected by Meta-TGDR. This
extension permits broader applicability and allows a comparison between the
predictive performance of Meta-TGDR and TGDR using an independent testing set.
Results
Using real-world applications, we demonstrated the proposed multi-TGDR
framework works well and the number of selected genes is less than the sum of
all individualized binary TGDRs. Additionally, Meta-TGDR and TGDR on the
batch-effect adjusted pooled data approximately provided same results. By
adding Bagging procedure in each application, the stability and good predictive
performance are warranted.
Conclusions
Compared with Meta-TGDR, TGDR is less computing time intensive, and requires
no samples of all classes in each study. On the adjusted data, it has
approximate same predictive performance with Meta-TGDR. Thus, it is highly
recommended
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