2,587 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
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification
We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include the support vector machine, Gaussian process classifiers, random subspace of adaboost, random subspace of rotation boosting, and deep learning classifiers. We demonstrate that a heterogeneous ensemble based on the simple fusion by sum rule of different classifiers performs consistently well across all twenty-five datasets. The most important result of our investigation is demonstrating that some very recent approaches, including the heterogeneous ensemble we propose in this paper, are capable of outperforming an SVM classifier (implemented with LibSVM), even when both kernel selection and SVM parameters are carefully tuned for each dataset
Efficient Diverse Ensemble for Discriminative Co-Tracking
Ensemble discriminative tracking utilizes a committee of classifiers, to
label data samples, which are in turn, used for retraining the tracker to
localize the target using the collective knowledge of the committee. Committee
members could vary in their features, memory update schemes, or training data,
however, it is inevitable to have committee members that excessively agree
because of large overlaps in their version space. To remove this redundancy and
have an effective ensemble learning, it is critical for the committee to
include consistent hypotheses that differ from one-another, covering the
version space with minimum overlaps. In this study, we propose an online
ensemble tracker that directly generates a diverse committee by generating an
efficient set of artificial training. The artificial data is sampled from the
empirical distribution of the samples taken from both target and background,
whereas the process is governed by query-by-committee to shrink the overlap
between classifiers. The experimental results demonstrate that the proposed
scheme outperforms conventional ensemble trackers on public benchmarks.Comment: CVPR 2018 Submissio
Transcription Factor-DNA Binding Via Machine Learning Ensembles
We present ensemble methods in a machine learning (ML) framework combining
predictions from five known motif/binding site exploration algorithms. For a
given TF the ensemble starts with position weight matrices (PWM's) for the
motif, collected from the component algorithms. Using dimension reduction, we
identify significant PWM-based subspaces for analysis. Within each subspace a
machine classifier is built for identifying the TF's gene (promoter) targets
(Problem 1). These PWM-based subspaces form an ML-based sequence analysis tool.
Problem 2 (finding binding motifs) is solved by agglomerating k-mer (string)
feature PWM-based subspaces that stand out in identifying gene targets. We
approach Problem 3 (binding sites) with a novel machine learning approach that
uses promoter string features and ML importance scores in a classification
algorithm locating binding sites across the genome. For target gene
identification this method improves performance (measured by the F1 score) by
about 10 percentage points over the (a) motif scanning method and (b) the
coexpression-based association method. Top motif outperformed 5 component
algorithms as well as two other common algorithms (BEST and DEME). For
identifying individual binding sites on a benchmark cross species database
(Tompa et al., 2005) we match the best performer without much human
intervention. It also improved the performance on mammalian TFs.
The ensemble can integrate orthogonal information from different weak
learners (potentially using entirely different types of features) into a
machine learner that can perform consistently better for more TFs. The TF gene
target identification component (problem 1 above) is useful in constructing a
transcriptional regulatory network from known TF-target associations. The
ensemble is easily extendable to include more tools as well as future PWM-based
information.Comment: 33 page
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