7,476 research outputs found
A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction
This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalized rule induction. The framework emphasizes the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization
Porting Decision Tree Algorithms to Multicore using FastFlow
The whole computer hardware industry embraced multicores. For these machines,
the extreme optimisation of sequential algorithms is no longer sufficient to
squeeze the real machine power, which can be only exploited via thread-level
parallelism. Decision tree algorithms exhibit natural concurrency that makes
them suitable to be parallelised. This paper presents an approach for
easy-yet-efficient porting of an implementation of the C4.5 algorithm on
multicores. The parallel porting requires minimal changes to the original
sequential code, and it is able to exploit up to 7X speedup on an Intel
dual-quad core machine.Comment: 18 pages + cove
Decision Making in the Medical Domain: Comparing the Effectiveness of GP-Generated Fuzzy Intelligent Structures
ABSTRACT: In this work, we examine the effectiveness of two intelligent models in medical domains. Namely, we apply grammar-guided genetic programming to produce fuzzy intelligent structures, such as fuzzy rule-based systems and fuzzy Petri nets, in medical data mining tasks. First, we use two context-free grammars to describe fuzzy rule-based systems and fuzzy Petri nets with genetic programming. Then, we apply cellular encoding in order to express the fuzzy Petri nets with arbitrary size and topology. The models are examined thoroughly in four real-world medical data sets. Results are presented in detail and the competitive advantages and drawbacks of the selected methodologies are discussed, in respect to the nature of each application domain. Conclusions are drawn on the effectiveness and efficiency of the presented approach
A knowledge engineering approach to the recognition of genomic coding regions
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