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This paper offers a survey of recent work on particle swarm classification (PSC), a promising offshoot of particle swarm optimization (PSO), with the goal of positioning it in the overall classification domain. The richness of the related literature shows that this new classification approach may be an efficient alternative, in addition to existing paradigms. After describing the various PSC approaches found in the literature, the paper identifies and discusses two data-related problems that may affect PSC efficiency: high-dimensional datasets and mixed-attribute data. The solutions that have been proposed in the literature for each of these issues are described including recent improvements by a novel PSC algorithm developed by the authors. Subsequently, a positioning PSC for these problems with respect to other classification approaches is made. This is accomplished by using one proprietary and five well known benchmark datasets to determine the performances of PSC algorithm and comparing the obtained results with those reported for various other classification approaches. It is concluded that PSC can be efficiently applied to classification problems with large numbers of instances, both in continuous and mixed-attribute problem description spaces. Moreover, the obtained results show that PSC may not only be applied to more demanding problem domains, but it can also be a competitive alternative to well established classification techniques.