23,372 research outputs found
An efficient randomised sphere cover classifier
This paper describes an efficient randomised sphere cover classifier(aRSC), that reduces the training data set size without loss of accuracy when compared to nearest neighbour classifiers. The motivation for developing this algorithm is the desire to have a non-deterministic, fast, instance-based classifier that performs well in isolation but is also ideal for use with ensembles. We use 24 benchmark datasets from UCI repository and six gene expression datasets for evaluation. The first set of experiments demonstrate the basic benefits of sphere covering. The second set of experiments demonstrate that when we set the a parameter through cross validation, the resulting aRSC algorithm outperforms several well known classifiers when compared using the Friedman rank sum test. Thirdly, we test the usefulness of aRSC when used with three feature filtering filters on six gene expression datasets. Finally, we highlight the benefits of pruning with a bias/variance decompositio
Support Vector Machine classification of strong gravitational lenses
The imminent advent of very large-scale optical sky surveys, such as Euclid
and LSST, makes it important to find efficient ways of discovering rare objects
such as strong gravitational lens systems, where a background object is
multiply gravitationally imaged by a foreground mass. As well as finding the
lens systems, it is important to reject false positives due to intrinsic
structure in galaxies, and much work is in progress with machine learning
algorithms such as neural networks in order to achieve both these aims. We
present and discuss a Support Vector Machine (SVM) algorithm which makes use of
a Gabor filterbank in order to provide learning criteria for separation of
lenses and non-lenses, and demonstrate using blind challenges that under
certain circumstances it is a particularly efficient algorithm for rejecting
false positives. We compare the SVM engine with a large-scale human examination
of 100000 simulated lenses in a challenge dataset, and also apply the SVM
method to survey images from the Kilo-Degree Survey.Comment: Accepted by MNRA
Randomized Reference Classifier with Gaussian Distribution and Soft Confusion Matrix Applied to the Improving Weak Classifiers
In this paper, an issue of building the RRC model using probability
distributions other than beta distribution is addressed. More precisely, in
this paper, we propose to build the RRR model using the truncated normal
distribution. Heuristic procedures for expected value and the variance of the
truncated-normal distribution are also proposed. The proposed approach is
tested using SCM-based model for testing the consequences of applying the
truncated normal distribution in the RRC model. The experimental evaluation is
performed using four different base classifiers and seven quality measures. The
results showed that the proposed approach is comparable to the RRC model built
using beta distribution. What is more, for some base classifiers, the
truncated-normal-based SCM algorithm turned out to be better at discovering
objects coming from minority classes.Comment: arXiv admin note: text overlap with arXiv:1901.0882
Feature selection when there are many influential features
Recent discussion of the success of feature selection methods has argued that
focusing on a relatively small number of features has been counterproductive.
Instead, it is suggested, the number of significant features can be in the
thousands or tens of thousands, rather than (as is commonly supposed at
present) approximately in the range from five to fifty. This change, in orders
of magnitude, in the number of influential features, necessitates alterations
to the way in which we choose features and to the manner in which the success
of feature selection is assessed. In this paper, we suggest a general approach
that is suited to cases where the number of relevant features is very large,
and we consider particular versions of the approach in detail. We propose ways
of measuring performance, and we study both theoretical and numerical
properties of the proposed methodology.Comment: Published in at http://dx.doi.org/10.3150/13-BEJ536 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Learning from the machine: interpreting machine learning algorithms for point- and extended- source classification
We investigate star-galaxy classification for astronomical surveys in the
context of four methods enabling the interpretation of black-box machine
learning systems. The first is outputting and exploring the decision boundaries
as given by decision tree based methods, which enables the visualization of the
classification categories. Secondly, we investigate how the Mutual Information
based Transductive Feature Selection (MINT) algorithm can be used to perform
feature pre-selection. If one would like to provide only a small number of
input features to a machine learning classification algorithm, feature
pre-selection provides a method to determine which of the many possible input
properties should be selected. Third is the use of the tree-interpreter package
to enable popular decision tree based ensemble methods to be opened,
visualized, and understood. This is done by additional analysis of the tree
based model, determining not only which features are important to the model,
but how important a feature is for a particular classification given its value.
Lastly, we use decision boundaries from the model to revise an already existing
method of classification, essentially asking the tree based method where
decision boundaries are best placed and defining a new classification method.
We showcase these techniques by applying them to the problem of star-galaxy
separation using data from the Sloan Digital Sky Survey (hereafter SDSS). We
use the output of MINT and the ensemble methods to demonstrate how more complex
decision boundaries improve star-galaxy classification accuracy over the
standard SDSS frames approach (reducing misclassifications by up to
). We then show how tree-interpreter can be used to explore how
relevant each photometric feature is when making a classification on an object
by object basis.Comment: 12 pages, 8 figures, 8 table
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