393 research outputs found
Multiclass Approaches for Support Vector Machine Based Land Cover Classification
SVMs were initially developed to perform binary classification; though,
applications of binary classification are very limited. Most of the practical
applications involve multiclass classification, especially in remote sensing
land cover classification. A number of methods have been proposed to implement
SVMs to produce multiclass classification. A number of methods to generate
multiclass SVMs from binary SVMs have been proposed by researchers and is still
a continuing research topic. This paper compares the performance of six
multi-class approaches to solve classification problem with remote sensing data
in term of classification accuracy and computational cost. One vs. one, one vs.
rest, Directed Acyclic Graph (DAG), and Error Corrected Output Coding (ECOC)
based multiclass approaches creates many binary classifiers and combines their
results to determine the class label of a test pixel. Another catogery of multi
class approach modify the binary class objective function and allows
simultaneous computation of multiclass classification by solving a single
optimisation problem. Results from this study conclude the usefulness of One
vs. One multi class approach in term of accuracy and computational cost over
other multi class approaches.Comment: 16 pages, MapIndia 2005 conferenc
A Novel Approach to Distributed Multi-Class SVM
With data sizes constantly expanding, and with classical machine learning
algorithms that analyze such data requiring larger and larger amounts of
computation time and storage space, the need to distribute computation and
memory requirements among several computers has become apparent. Although
substantial work has been done in developing distributed binary SVM algorithms
and multi-class SVM algorithms individually, the field of multi-class
distributed SVMs remains largely unexplored. This research proposes a novel
algorithm that implements the Support Vector Machine over a multi-class dataset
and is efficient in a distributed environment (here, Hadoop). The idea is to
divide the dataset into half recursively and thus compute the optimal Support
Vector Machine for this half during the training phase, much like a divide and
conquer approach. While testing, this structure has been effectively exploited
to significantly reduce the prediction time. Our algorithm has shown better
computation time during the prediction phase than the traditional sequential
SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of
the dataset grows. This approach also classifies the data with higher accuracy
than the traditional multi-class algorithms.Comment: 8 Page
Alternating direction method of multipliers for regularized multiclass support vector machines
The support vector machine (SVM) was originally designed for binary
classifications. A lot of effort has been put to generalize the binary SVM to
multiclass SVM (MSVM) which are more complex problems. Initially, MSVMs were
solved by considering their dual formulations which are quadratic programs and
can be solved by standard second-order methods. However, the duals of MSVMs
with regularizers are usually more difficult to formulate and computationally
very expensive to solve. This paper focuses on several regularized MSVMs and
extends the alternating direction method of multiplier (ADMM) to these MSVMs.
Using a splitting technique, all considered MSVMs are written as two-block
convex programs, for which the ADMM has global convergence guarantees.
Numerical experiments on synthetic and real data demonstrate the high
efficiency and accuracy of our algorithms.Comment: in Lecture Notes in Computer Science (LNCS) 201
DCSVM: Fast Multi-class Classification using Support Vector Machines
We present DCSVM, an efficient algorithm for multi-class classification using
Support Vector Machines. DCSVM is a divide and conquer algorithm which relies
on data sparsity in high dimensional space and performs a smart partitioning of
the whole training data set into disjoint subsets that are easily separable. A
single prediction performed between two partitions eliminates at once one or
more classes in one partition, leaving only a reduced number of candidate
classes for subsequent steps. The algorithm continues recursively, reducing the
number of classes at each step, until a final binary decision is made between
the last two classes left in the competition. In the best case scenario, our
algorithm makes a final decision between classes in decision
steps and in the worst case scenario DCSVM makes a final decision in
steps, which is not worse than the existent techniques
Recognizing Static Signs from the Brazilian Sign Language: Comparing Large-Margin Decision Directed Acyclic Graphs, Voting Support Vector Machines and Artificial Neural Networks
In this paper, we explore and detail our experiments in a
high-dimensionality, multi-class image classification problem often found in
the automatic recognition of Sign Languages. Here, our efforts are directed
towards comparing the characteristics, advantages and drawbacks of creating and
training Support Vector Machines disposed in a Directed Acyclic Graph and
Artificial Neural Networks to classify signs from the Brazilian Sign Language
(LIBRAS). We explore how the different heuristics, hyperparameters and
multi-class decision schemes affect the performance, efficiency and ease of use
for each classifier. We provide hyperparameter surface maps capturing accuracy
and efficiency, comparisons between DDAGs and 1-vs-1 SVMs, and effects of
heuristics when training ANNs with Resilient Backpropagation. We report
statistically significant results using Cohen's Kappa statistic for contingency
tables.Comment: 6 page
Optimized Method for Iranian Road Signs Detection and recognition system
Road sign recognition is one of the core technologies in Intelligent
Transport Systems. In the current study, a robust and real-time method is
presented to identify and detect the roads speed signs in road image in
different situations. In our proposed method, first, the connected components
are created in the main image using the edge detection and mathematical
morphology and the location of the road signs extracted by the geometric and
color data; then the letters are segmented and recognized by Multiclass Support
Vector Machine (SVMs) classifiers. Regarding that the geometric and color
features ate properly used in detection the location of the road signs, so it
is not sensitive to the distance and noise and has higher speed and efficiency.
In the result part, the proposed approach is applied on Iranian road speed sign
database and the detection and recognition accuracy rate achieved 98.66% and
100% respectively
Enhancements of Multi-class Support Vector Machine Construction from Binary Learners using Generalization Performance
We propose several novel methods for enhancing the multi-class SVMs by
applying the generalization performance of binary classifiers as the core idea.
This concept will be applied on the existing algorithms, i.e., the Decision
Directed Acyclic Graph (DDAG), the Adaptive Directed Acyclic Graphs (ADAG), and
Max Wins. Although in the previous approaches there have been many attempts to
use some information such as the margin size and the number of support vectors
as performance estimators for binary SVMs, they may not accurately reflect the
actual performance of the binary SVMs. We show that the generalization ability
evaluated via a cross-validation mechanism is more suitable to directly extract
the actual performance of binary SVMs. Our methods are built around this
performance measure, and each of them is crafted to overcome the weakness of
the previous algorithm. The proposed methods include the Reordering Adaptive
Directed Acyclic Graph (RADAG), Strong Elimination of the classifiers (SE),
Weak Elimination of the classifiers (WE), and Voting based Candidate Filtering
(VCF). Experimental results demonstrate that our methods give significantly
higher accuracy than all of the traditional ones. Especially, WE provides
significantly superior results compared to Max Wins which is recognized as the
state of the art algorithm in terms of both accuracy and classification speed
with two times faster in average.Comment: 17 pages, 13 figure
Efficient Decision Trees for Multi-class Support Vector Machines Using Entropy and Generalization Error Estimation
We propose new methods for Support Vector Machines (SVMs) using tree
architecture for multi-class classi- fication. In each node of the tree, we
select an appropriate binary classifier using entropy and generalization error
estimation, then group the examples into positive and negative classes based on
the selected classi- fier and train a new classifier for use in the
classification phase. The proposed methods can work in time complexity between
O(log2N) to O(N) where N is the number of classes. We compared the performance
of our proposed methods to the traditional techniques on the UCI machine
learning repository using 10-fold cross-validation. The experimental results
show that our proposed methods are very useful for the problems that need fast
classification time or problems with a large number of classes as the proposed
methods run much faster than the traditional techniques but still provide
comparable accuracy
Fast Meta-Learning for Adaptive Hierarchical Classifier Design
We propose a new splitting criterion for a meta-learning approach to
multiclass classifier design that adaptively merges the classes into a
tree-structured hierarchy of increasingly difficult binary classification
problems. The classification tree is constructed from empirical estimates of
the Henze-Penrose bounds on the pairwise Bayes misclassification rates that
rank the binary subproblems in terms of difficulty of classification. The
proposed empirical estimates of the Bayes error rate are computed from the
minimal spanning tree (MST) of the samples from each pair of classes. Moreover,
a meta-learning technique is presented for quantifying the one-vs-rest Bayes
error rate for each individual class from a single MST on the entire dataset.
Extensive simulations on benchmark datasets show that the proposed hierarchical
method can often be learned much faster than competing methods, while achieving
competitive accuracy.Comment: Code available at: https://github.com/HeroResearchGroup/SmartSV
Optimal arrangements of hyperplanes for multiclass classification
In this paper, we present a novel approach to construct multiclass
classifiers by means of arrangements of hyperplanes. We propose different mixed
integer (linear and non linear) programming formulations for the problem using
extensions of widely used measures for misclassifying observations where the
\textit{kernel trick} can be adapted to be applicable. Some dimensionality
reductions and variable fixing strategies are also developed for these models.
An extensive battery of experiments has been run which reveal the powerfulness
of our proposal as compared with other previously proposed methodologies.Comment: 8 Figures, 2 Table
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