54,125 research outputs found
Dissimilarity-based Ensembles for Multiple Instance Learning
In multiple instance learning, objects are sets (bags) of feature vectors
(instances) rather than individual feature vectors. In this paper we address
the problem of how these bags can best be represented. Two standard approaches
are to use (dis)similarities between bags and prototype bags, or between bags
and prototype instances. The first approach results in a relatively
low-dimensional representation determined by the number of training bags, while
the second approach results in a relatively high-dimensional representation,
determined by the total number of instances in the training set. In this paper
a third, intermediate approach is proposed, which links the two approaches and
combines their strengths. Our classifier is inspired by a random subspace
ensemble, and considers subspaces of the dissimilarity space, defined by
subsets of instances, as prototypes. We provide guidelines for using such an
ensemble, and show state-of-the-art performances on a range of multiple
instance learning problems.Comment: Submitted to IEEE Transactions on Neural Networks and Learning
Systems, Special Issue on Learning in Non-(geo)metric Space
On Classification with Bags, Groups and Sets
Many classification problems can be difficult to formulate directly in terms
of the traditional supervised setting, where both training and test samples are
individual feature vectors. There are cases in which samples are better
described by sets of feature vectors, that labels are only available for sets
rather than individual samples, or, if individual labels are available, that
these are not independent. To better deal with such problems, several
extensions of supervised learning have been proposed, where either training
and/or test objects are sets of feature vectors. However, having been proposed
rather independently of each other, their mutual similarities and differences
have hitherto not been mapped out. In this work, we provide an overview of such
learning scenarios, propose a taxonomy to illustrate the relationships between
them, and discuss directions for further research in these areas
Supervised Classification: Quite a Brief Overview
The original problem of supervised classification considers the task of
automatically assigning objects to their respective classes on the basis of
numerical measurements derived from these objects. Classifiers are the tools
that implement the actual functional mapping from these measurements---also
called features or inputs---to the so-called class label---or output. The
fields of pattern recognition and machine learning study ways of constructing
such classifiers. The main idea behind supervised methods is that of learning
from examples: given a number of example input-output relations, to what extent
can the general mapping be learned that takes any new and unseen feature vector
to its correct class? This chapter provides a basic introduction to the
underlying ideas of how to come to a supervised classification problem. In
addition, it provides an overview of some specific classification techniques,
delves into the issues of object representation and classifier evaluation, and
(very) briefly covers some variations on the basic supervised classification
task that may also be of interest to the practitioner
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Multi-class protein fold classification using a new ensemble machine learning approach.
Protein structure classification represents an important process in understanding the associations
between sequence and structure as well as possible functional and evolutionary relationships.
Recent structural genomics initiatives and other high-throughput experiments have populated the
biological databases at a rapid pace. The amount of structural data has made traditional methods
such as manual inspection of the protein structure become impossible. Machine learning has been
widely applied to bioinformatics and has gained a lot of success in this research area. This work
proposes a novel ensemble machine learning method that improves the coverage of the classifiers
under the multi-class imbalanced sample sets by integrating knowledge induced from different base
classifiers, and we illustrate this idea in classifying multi-class SCOP protein fold data. We have
compared our approach with PART and show that our method improves the sensitivity of the
classifier in protein fold classification. Furthermore, we have extended this method to learning over
multiple data types, preserving the independence of their corresponding data sources, and show
that our new approach performs at least as well as the traditional technique over a single joined
data source. These experimental results are encouraging, and can be applied to other bioinformatics
problems similarly characterised by multi-class imbalanced data sets held in multiple data
sources
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