In an effort to improve classification accuracy, it is often desired to incorporate the results from multiple classifiers that are varied in terms of their approach or algorithm.
There is no single approach or algorithm that can be applied to all cases.
Projects can develop new approaches or better algorithms, and/or implement/adapt existing techniques for real application settings.
The obtained results via the two presented approaches are better than those obtained via either literature approaches or programmed algorithms for all studied cases.
Soft-computing approaches have then been successfully applied to solve the SSRP in the past, such as evolutionary computation techniques, variable neighborhood approaches or tabu search algorithms.
In the framework of DFT approaches, line frequency synchronization techniques or algorithms to compensate for desynchronization are necessary; alternative approaches such as those based on the Prony and ESPRIT methods are not sensitive to desynchronization, but they often require significant computational burden.
In fact, combining the TS algorithm and the TR approach or the SRM and ACF algorithms presents an overall estimation complexity of the three parameters around N s N a 2 + N a ( N s - 1 ) ( L + 1 ) 3 floating-point operations, whereas our approach provides accurate estimates for the three desired parameters with a single algorithm and a lower computational complexity.
For comparison, we also investigated the performances of several other approaches or predictors, including GPS 1.1 algorithm [24], PoPS [19], [20], SitesPrediction [21] and CaMPDB [22].
A universally superior approach or family of algorithms could not be identified with respect to the achieved rankings.
The deliberations of LTS'09 included presentations and reviews of different approaches (algorithms, biomarkers, or measures) for identifying asymptomatic individuals at elevated risk for AD who would be candidates for longitudinal or prevention studies.
