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Parametric Optimization In Data Mining Incorporated With Ga-Based Search, L Tan, D Taniar, K Smith
Parametric Optimization In Data Mining Incorporated With Ga-Based Search, L Tan, D Taniar, K Smith
Dr Ling Tan
A number of parameters must be specified for a data-mining algorithm. Default values of these parameters are given and generally accepted as ‘good’ estimates for any data set. However, data mining models are known to be data dependent, and so are for their parameters. Default values may be good estimates, but they are often not the best parameter values for a particular data set. A tuned set of parameter values is able to produce a data-mining model of better classification and higher prediction accuracy. However parameter search is known to be expensive. This paper investigates GA-based heuristic techniques in a …
A Taxonomy For Inter-Model Parallelism In High Performance Data Mining, Ling Tan, D Taniar, K Smith
A Taxonomy For Inter-Model Parallelism In High Performance Data Mining, Ling Tan, D Taniar, K Smith
Dr Ling Tan
No abstract provided.
Dynamic Task Assignment In Server Farms: Better Performance By Task Grouping, Ling Tan, Z Tari
Dynamic Task Assignment In Server Farms: Better Performance By Task Grouping, Ling Tan, Z Tari
Dr Ling Tan
This paper describes a dynamic load balancing approach to distributed server farm systems. This approach overcomes the interference caused by non-negligible very-large tasks in the heavy-tailed distribution. First, a subset of tasks is allocated proportionally to the processing capability of participating servers by taking into account their remaining processing time. Later, tasks in the servers are processed in order of priority to optimise the system response time. The proposed load balancing algorithm also takes into account the information on server loads to avoid load imbalance caused by very large tasks. The experiments show that the mean waiting time and the …
A New Parallel Genetic Algorithm, Ling Tan, D Taniar, K Smith
A New Parallel Genetic Algorithm, Ling Tan, D Taniar, K Smith
Dr Ling Tan
One problem of propagating the globally fittest individual via neighbourhood evolution in both the island model and the cellular model of existing parallel genetic algorithms (PGAs) is that the migration of the globally best individual is delayed to non-adjacent processors. This may cause an inferior search in those sub-populations. The propagation delay of the globally best individual is proportional to the network distance between two processors. Delayed migration of the best individual in PGAs is an essential deviation from the sequential version of the genetic algorithm, in which the best individuals are always used to compete with other individuals. To …