2,554 research outputs found
On the design of an ECOC-compliant genetic algorithm
Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches
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
Turbo Decoding and Detection for Wireless Applications
A historical perspective of turbo coding and turbo transceivers inspired by the generic turbo principles is provided, as it evolved from Shannonâs visionary predictions. More specifically, we commence by discussing the turbo principles, which have been shown to be capable of performing close to Shannonâs capacity limit. We continue by reviewing the classic maximum a posteriori probability decoder. These discussions are followed by studying the effect of a range of system parameters in a systematic fashion, in order to gauge their performance ramifications. In the second part of this treatise, we focus our attention on the family of iterative receivers designed for wireless communication systems, which were partly inspired by the invention of turbo codes. More specifically, the family of iteratively detected joint coding and modulation schemes, turbo equalization, concatenated spacetime and channel coding arrangements, as well as multi-user detection and three-stage multimedia systems are highlighted
Decoder-in-the-Loop: Genetic Optimization-based LDPC Code Design
LDPC code design tools typically rely on asymptotic code behavior and are
affected by an unavoidable performance degradation due to model imperfections
in the short length regime. We propose an LDPC code design scheme based on an
evolutionary algorithm, the Genetic Algorithm (GenAlg), implementing a
"decoder-in-the-loop" concept. It inherently takes into consideration the
channel, code length and the number of iterations while optimizing the
error-rate of the actual decoder hardware architecture. We construct short
length LDPC codes (i.e., the parity-check matrix) with error-rate performance
comparable to, or even outperforming that of well-designed standardized short
length LDPC codes over both AWGN and Rayleigh fading channels. Our proposed
algorithm can be used to design LDPC codes with special graph structures (e.g.,
accumulator-based codes) to facilitate the encoding step, or to satisfy any
other practical requirement. Moreover, GenAlg can be used to design LDPC codes
with the aim of reducing decoding latency and complexity, leading to coding
gains of up to dB and dB at BLER of for both AWGN and
Rayleigh fading channels, respectively, when compared to state-of-the-art short
LDPC codes. Also, we analyze what can be learned from the resulting codes and,
as such, the GenAlg particularly highlights design paradigms of short length
LDPC codes (e.g., codes with degree-1 variable nodes obtain very good results).Comment: in IEEE Access, 201
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