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ICMLA
2008
13 years 6 months ago
Predicting Algorithm Accuracy with a Small Set of Effective Meta-Features
We revisit 26 meta-features typically used in the context of meta-learning for model selection. Using visual analysis and computational complexity considerations, we find 4 meta-f...
Jun Won Lee, Christophe G. Giraud-Carrier
JSS
2008
157views more  JSS 2008»
13 years 4 months ago
Can k-NN imputation improve the performance of C4.5 with small software project data sets? A comparative evaluation
Missing data is a widespread problem that can affect the ability to use data to construct effective prediction systems. We investigate a common machine learning technique that can...
Qinbao Song, Martin J. Shepperd, Xiangru Chen, Jun...
IEE
2002
72views more  IEE 2002»
13 years 4 months ago
Making inferences with small numbers of training sets
This paper discusses a potential methodological problem with empirical studies assessing project effort prediction systems. Frequently a hold-out strategy is deployed so that the ...
Colin Kirsopp, Martin J. Shepperd
DKE
2007
89views more  DKE 2007»
13 years 4 months ago
The effect of threshold values on association rule based classification accuracy
Classification Association Rule Mining (CARM) systems operate by applying an Association Rule Mining (ARM) method to obtain classification rules from a training set of previousl...
Frans Coenen, Paul H. Leng
ACIIDS
2010
IEEE
170views Database» more  ACIIDS 2010»
13 years 2 months ago
On the Effectiveness of Gene Selection for Microarray Classification Methods
Microarray data usually contains a high level of noisy gene data, the noisy gene data include incorrect, noise and irrelevant genes. Before Microarray data classification takes pla...
Zhongwei Zhang, Jiuyong Li, Hong Hu, Hong Zhou