7,809 research outputs found
Sub-Classifier Construction for Error Correcting Output Code Using Minimum Weight Perfect Matching
Multi-class classification is mandatory for real world problems and one of
promising techniques for multi-class classification is Error Correcting Output
Code. We propose a method for constructing the Error Correcting Output Code to
obtain the suitable combination of positive and negative classes encoded to
represent binary classifiers. The minimum weight perfect matching algorithm is
applied to find the optimal pairs of subset of classes by using the
generalization performance as a weighting criterion. Based on our method, each
subset of classes with positive and negative labels is appropriately combined
for learning the binary classifiers. Experimental results show that our
technique gives significantly higher performance compared to traditional
methods including the dense random code and the sparse random code both in
terms of accuracy and classification times. Moreover, our method requires
significantly smaller number of binary classifiers while maintaining accuracy
compared to the One-Versus-One.Comment: 7 pages, 3 figure
RandomBoost: Simplified Multi-class Boosting through Randomization
We propose a novel boosting approach to multi-class classification problems,
in which multiple classes are distinguished by a set of random projection
matrices in essence. The approach uses random projections to alleviate the
proliferation of binary classifiers typically required to perform multi-class
classification. The result is a multi-class classifier with a single
vector-valued parameter, irrespective of the number of classes involved. Two
variants of this approach are proposed. The first method randomly projects the
original data into new spaces, while the second method randomly projects the
outputs of learned weak classifiers. These methods are not only conceptually
simple but also effective and easy to implement. A series of experiments on
synthetic, machine learning and visual recognition data sets demonstrate that
our proposed methods compare favorably to existing multi-class boosting
algorithms in terms of both the convergence rate and classification accuracy.Comment: 15 page
Using Output Codes for Two-class Classification Problems
Error-correcting output codes (ECOCs) have been widely used in many applications for multi-class classification problems. The problem is that ECOCs cannot be ap- plied directly on two-class datasets. The goal of this thesis is to design and evaluate an approach to solve this problem, and then investigate whether the approach can yield better classification models. To be able to use ECOCs, we turn two-class datasets into multi-class datasets first, by using clustering. With the resulting multi-class datasets in hand, we evalu- ate three different encoding methods for ECOCs: exhaustive coding, random coding and a “pre-defined” code that is found using random search. The exhaustive coding method has the highest error-correcting abilities. However, this method is limited due to the exponential growth of bit columns in the codeword matrix precluding it from being used for problems with large numbers of classes. Random coding can be used to cover situations with large numbers of classes in the data. To improve on completely random matrices, “pre-defined” codeword matrices can be generated by using random search that optimizes row separation yielding better error correction than a purely random matrix. To speed up the process of finding good matrices, GPU parallel programming is investigated in this thesis. From the empirical results, we can say that the new algorithm, which applies multi-class ECOCs on two-class data using clustering, does improve the performance for some base learners, when compared to applying them directly to the original two- class datasets
Deep N-ary Error Correcting Output Codes
Ensemble learning consistently improves the performance of multi-class
classification through aggregating a series of base classifiers. To this end,
data-independent ensemble methods like Error Correcting Output Codes (ECOC)
attract increasing attention due to its easiness of implementation and
parallelization. Specifically, traditional ECOCs and its general extension
N-ary ECOC decompose the original multi-class classification problem into a
series of independent simpler classification subproblems. Unfortunately,
integrating ECOCs, especially N-ary ECOC with deep neural networks, termed as
deep N-ary ECOC, is not straightforward and yet fully exploited in the
literature, due to the high expense of training base learners. To facilitate
the training of N-ary ECOC with deep learning base learners, we further propose
three different variants of parameter sharing architectures for deep N-ary
ECOC. To verify the generalization ability of deep N-ary ECOC, we conduct
experiments by varying the backbone with different deep neural network
architectures for both image and text classification tasks. Furthermore,
extensive ablation studies on deep N-ary ECOC show its superior performance
over other deep data-independent ensemble methods.Comment: EAI MOBIMEDIA 202
Locally Non-linear Embeddings for Extreme Multi-label Learning
The objective in extreme multi-label learning is to train a classifier that
can automatically tag a novel data point with the most relevant subset of
labels from an extremely large label set. Embedding based approaches make
training and prediction tractable by assuming that the training label matrix is
low-rank and hence the effective number of labels can be reduced by projecting
the high dimensional label vectors onto a low dimensional linear subspace.
Still, leading embedding approaches have been unable to deliver high prediction
accuracies or scale to large problems as the low rank assumption is violated in
most real world applications.
This paper develops the X-One classifier to address both limitations. The
main technical contribution in X-One is a formulation for learning a small
ensemble of local distance preserving embeddings which can accurately predict
infrequently occurring (tail) labels. This allows X-One to break free of the
traditional low-rank assumption and boost classification accuracy by learning
embeddings which preserve pairwise distances between only the nearest label
vectors.
We conducted extensive experiments on several real-world as well as benchmark
data sets and compared our method against state-of-the-art methods for extreme
multi-label classification. Experiments reveal that X-One can make
significantly more accurate predictions then the state-of-the-art methods
including both embeddings (by as much as 35%) as well as trees (by as much as
6%). X-One can also scale efficiently to data sets with a million labels which
are beyond the pale of leading embedding methods
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