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» A SVM Regression Based Approach to Filling in Missing Values
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KES
2005
Springer
13 years 10 months ago
A SVM Regression Based Approach to Filling in Missing Values
In KDD procedure, to fill in missing data typically requires a very large investment of time and energy - often 80% to 90% of a data analysis project is spent in making the data re...
Honghai Feng, Chen Guoshun, Yin Cheng, Bingru Yang...
TIFS
2010
103views more  TIFS 2010»
13 years 3 months ago
Addressing missing values in kernel-based multimodal biometric fusion using neutral point substitution
In multimodal biometric information fusion, it is common to encounter missing modalities in which matching cannot be performed. As a result, at the match score level, this implies...
Norman Poh, David Windridge, Vadim Mottl, Alexande...
BMCBI
2006
211views more  BMCBI 2006»
13 years 4 months ago
Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding s
Background: Gene expression profiling has become a useful biological resource in recent years, and it plays an important role in a broad range of areas in biology. The raw gene ex...
Xian Wang, Ao Li, Zhaohui Jiang, Huanqing Feng
KDD
2007
ACM
182views Data Mining» more  KDD 2007»
14 years 5 months ago
Cleaning disguised missing data: a heuristic approach
In some applications such as filling in a customer information form on the web, some missing values may not be explicitly represented as such, but instead appear as potentially va...
Ming Hua, Jian Pei
BMCBI
2010
110views more  BMCBI 2010»
13 years 4 months ago
Missing value imputation for epistatic MAPs
Background: Epistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The dat...
Colm Ryan, Derek Greene, Gerard Cagney, Padraig Cu...