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Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene...
The curse of dimensionality, caused by high-dimensionality gene and small-size sample of gene expression dataset, may degrade the accuracy of tumor classification. To solve the issue, in this paper, the neighborhood rough set theory is introduced, and through expanding neighborhood threshold, the relative neighborhood rough set theory is proposed. Some corresponding theorems are drawn, and a gene...
This paper presents a model to adjust timbre of software musical instrument by changing timbre parameters. The model is based on sine interpolation. It includes two sub models, vibration and amplitude envelope. To reflect the frequency spectrum, the model provides several parameters: offset coefficient, amplitude coefficient and frequency coefficient. The corresponding frequency spectrum of each parameter...
The disadvantages of the recent reduction algorithms are analyzed deeply. A new measure to knowledge and rough set is introduced to discuss the rough entropy of knowledge and the roughness of rough set. Based on this entropy, the new significance of attribute is defined and a heuristic algorithm of knowledge reduction is proposed and compared with two methods of attribute reduction which are based...
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