ATTRIBUTE NOISE DETECTION USING MULTI-RESOLUTION ANALYSIS

The value of knowledge inferred from information databases is critically dependent on the quality of data. The identification of noisy attributes which can easily corrupt and curtail valuable knowledge and information from a dataset can be very helpful to analysts. We present a novel detection method to identify noisy attributes in datasets of software metrics using multi-resolution transformations based on Discrete Wavelet Transforms. The proposed method has been applied to supervised datasets of scientific full-scale data from NASA's Software Metric Data Program (MDP) and to a military command, control, and communications system (CCCS). Empirical results have been favorably compared to those obtained from the robust Pairwise Attribute Noise Detection Algorithm (PANDA) using the same MDP datasets and with mixed results for the CCCS data. All results were verified with several case studies that included injecting known simulated noise into specific attributes with no class noise.