Abstract
Data characteristics are critical to prediction effectiveness, especially for the long-tailed regression problem and the specific attribute distortion problem. However, the current techniques are applied to the general prediction tasks without the ability to deal with such specific data characteristics. Both density bagging and bin-resampling techniques are developed respectively to solve the long-tailed regression problem. However, both two techniques pay for accuracy in the head and even the central part of the long-tailed distribution. This thesis addresses two different hybrid methods corresponding to density bagging and bin-resampling respectively, which can improve the prediction performance for the tail part of the long-tailed distribution without sacrificing more prediction accuracy for the head and even the central part. Three datasets are finally taken to evaluate the performance of our proposed techniques and their hybrid methods respectively and compared with several ensemble methods. A data characteristic of a specific attribute distortion problem indicates that an observe outcome of an instance corresponding to an input attribute is not always the true outcome in real world applications. We develop a state populate bagging to solve the specific attribute distortion in classification analysis. We first transform several true datasets into observed datasets according to the distortion matrices corresponding to their specific attributes, and afterwards transform each one of them into a possible true dataset according the reverse distortion matrices. Next step is to sample several same size sets with replacement on each one of possible true datasets. State populate bagging with two voting layers not only practices an observed outcome into possible true outcomes but also captures ensemble gain for any classifying algorithms without limiting to only a specific one. Finally, several true data sets from UCI machine repository are taken to reverse true data sets into observed data sets, and afterwards we evaluate the performance of state populate bagging and compared with several benchmark algorithms.