2,898 research outputs found
Analysis of the Correlation Between Majority Voting Error and the Diversity Measures in Multiple Classifier Systems
Combining classifiers by majority voting (MV) has
recently emerged as an effective way of improving
performance of individual classifiers. However, the
usefulness of applying MV is not always observed and
is subject to distribution of classification outputs in a
multiple classifier system (MCS). Evaluation of MV
errors (MVE) for all combinations of classifiers in MCS
is a complex process of exponential complexity.
Reduction of this complexity can be achieved provided
the explicit relationship between MVE and any other
less complex function operating on classifier outputs is
found. Diversity measures operating on binary
classification outputs (correct/incorrect) are studied in
this paper as potential candidates for such functions.
Their correlation with MVE, interpreted as the quality
of a measure, is thoroughly investigated using artificial
and real-world datasets. Moreover, we propose new
diversity measure efficiently exploiting information
coming from the whole MCS, rather than its part, for
which it is applied
An Overview of Classifier Fusion Methods
A number of classifier fusion methods have been
recently developed opening an alternative approach
leading to a potential improvement in the
classification performance. As there is little theory of
information fusion itself, currently we are faced with
different methods designed for different problems and
producing different results. This paper gives an
overview of classifier fusion methods and attempts to
identify new trends that may dominate this area of
research in future. A taxonomy of fusion methods
trying to bring some order into the existing âpudding
of diversitiesâ is also provided
A random forest system combination approach for error detection in digital dictionaries
When digitizing a print bilingual dictionary, whether via optical character
recognition or manual entry, it is inevitable that errors are introduced into
the electronic version that is created. We investigate automating the process
of detecting errors in an XML representation of a digitized print dictionary
using a hybrid approach that combines rule-based, feature-based, and language
model-based methods. We investigate combining methods and show that using
random forests is a promising approach. We find that in isolation, unsupervised
methods rival the performance of supervised methods. Random forests typically
require training data so we investigate how we can apply random forests to
combine individual base methods that are themselves unsupervised without
requiring large amounts of training data. Experiments reveal empirically that a
relatively small amount of data is sufficient and can potentially be further
reduced through specific selection criteria.Comment: 9 pages, 7 figures, 10 tables; appeared in Proceedings of the
Workshop on Innovative Hybrid Approaches to the Processing of Textual Data,
April 201
An Overview of Classifier Fusion Methods
A number of classifier fusion methods have been
recently developed opening an alternative approach
leading to a potential improvement in the
classification performance. As there is little theory of
information fusion itself, currently we are faced with
different methods designed for different problems and
producing different results. This paper gives an
overview of classifier fusion methods and attempts to
identify new trends that may dominate this area of
research in future. A taxonomy of fusion methods
trying to bring some order into the existing âpudding
of diversitiesâ is also provided
Improved Training for Self-Training by Confidence Assessments
It is well known that for some tasks, labeled data sets may be hard to
gather. Therefore, we wished to tackle here the problem of having insufficient
training data. We examined learning methods from unlabeled data after an
initial training on a limited labeled data set. The suggested approach can be
used as an online learning method on the unlabeled test set. In the general
classification task, whenever we predict a label with high enough confidence,
we treat it as a true label and train the data accordingly. For the semantic
segmentation task, a classic example for an expensive data labeling process, we
do so pixel-wise. Our suggested approaches were applied on the MNIST data-set
as a proof of concept for a vision classification task and on the ADE20K
data-set in order to tackle the semi-supervised semantic segmentation problem
Fast and Accurate 3D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region classifiers
In this paper we present a new robust approach for 3D face registration to an intrinsic coordinate system of the face. The intrinsic coordinate system is defined by the vertical symmetry plane through the nose, the tip of the nose and the slope of the bridge of the nose. In addition, we propose a 3D face classifier based on the fusion of many dependent region classifiers for overlapping face regions. The region classifiers use PCA-LDA for feature extraction and the likelihood ratio as a matching score. Fusion is realised using straightforward majority voting for the identification scenario. For verification, a voting approach is used as well and the decision is defined by comparing the number of votes to a threshold. Using the proposed registration method combined with a classifier consisting of 60 fused region classifiers we obtain a 99.0% identification rate on the all vs first identification test of the FRGC v2 data. A verification rate of 94.6% at FAR=0.1% was obtained for the all vs all verification test on the FRGC v2 data using fusion of 120 region classifiers. The first is the highest reported performance and the second is in the top-5 of best performing systems on these tests. In addition, our approach is much faster than other methods, taking only 2.5 seconds per image for registration and less than 0.1 ms per comparison. Because we apply feature extraction using PCA and LDA, the resulting template size is also very small: 6 kB for 60 region classifiers
Sparse detector sensor: Profiling experiments for broad-scale classification
This paper presents a simple prototype sparse detector imaging sensor built using sixteen off-the-shelf, retro-reflective, infrared-sensing elements placed at five-inch intervals in a vertical configuration. Profiling experiments for broad-scale classification of objects, such as humans, humans wearing large backpacks, and humans wearing small backpacks were conducted from data acquired from the sensor. Empirical analysis on models developed using fusion of various classifiers based on a diversity measure shows over ninety percent (90.07%) accuracy (using 10-fold cross validation) in categorizing sensed data into specific classes of interest, such as, humans wearing a large backpack. The results demonstrate that shadow images of sufficient resolution can be captured by the sensor such that broad-scale classification of objects is feasible. The sensor appears to be a low-cost alternative to traditional, high-resolution imaging sensors, and, after industrial packaging, it may be a good candidate for deployment in vast geographic regions in which low-cost, unattended ground sensors with highly accurate classification algorithms are needed
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