Block addressing indices for approximate text retrieval
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
A class of data structures for associative searching
PODS '84 Proceedings of the 3rd ACM SIGACT-SIGMOD symposium on Principles of database systems
Learning with Genetic Algorithms: An Overview
Machine Learning
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Stock trend prediction based on fractal feature selection and support vector machine
Expert Systems with Applications: An International Journal
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Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, adaptive control, and machine learning. Recently, exploiting fractal dimension to reduce the features of dataset is a novel method. FDR (Fractal Dimensionality Reduction), proposed by Traina in 2000, is the most famous fractal dimension based feature selection algorithm. However, it is intractable in the high dimensional data space for multiple scanning the dataset and incapable of eliminating two or more features simultaneously. In this paper we combine GA with the Z-ordering based FDR for addressing this problem and present a new algorithm GAZBFDR(Genetic Algorithm and Z-ordering Based FDR). The algorithm proposed can directly select the fixed number features from the feature space and utilize the fractal dimension variation to evaluate the selected features within the comparative lower space. The experimental results show that GAZBFDR algorithm achieves better performance in the high dimensional dataset.