Yi-Jun He

Integration Method of Ant Colony Algorithm and Rough Set Theory for Simultaneous Real Value Attribute Discretization and Attribute Reduction

Discretization of real value attributes (features) is an important pre-processing task in data mining, particularly for classification problems, and it has received significant attentions in machine learning community (Chmielewski & Grzymala-Busse, 1994; Dougherty et al., 1995; Nguyen & Skowron, 1995; Nguyen, 1998; Liu et al., 2002). Various studies have shown that discretization methods have the potential to reduce the amount of data while retaining or even improving predictive accuracy. Moreover, as reported in a study (Dougherty et al., 1995), discretization makes learning faster. However, most of the typical discretization methods can be considered as univariate discretization methods, which may fail to capture the correlation of attributes and result in degradation of the performance of a classification model. As reported (Liu et al., 2002), numerous discretization methods available in the literatures can be categorized in several dimensions: dynamic vs. static, local vs. global, splitting vs. merging, direct vs. incremental, and supervised vs. unsupervised. A hierarchical framework was also given to categorize the existing methods and pave the way for further development. A lot of work has been done, but still many issues remain unsolved, and new methods are needed (Liu et al. 2002). Since there are lots of discretization methods available, how does one evaluate discretization effects of various methods? In this study, we will focus on simplicity based criteria while preserving consistency, where simplicity is evaluated by the number of cuts. The fewer the number of cuts obtained by a discretization method, the better the effect of that method. Hence, real value attributes discretization can be defined as a problem of searching a global minimal set of cuts on attribute domains while preserving consistency, which has been shown as NP-hard problems (Nguyen, 1998). Rough set theory (Pawlak, 1982) has been considered as an effective mathematical tool for dealing with uncertain, imprecise and incomplete information and has been successfully applied in such fields as knowledge discovery, decision support, pattern classification, etc. However, rough set theory is just suitable to deal with discrete attributes, and it needs discretization as a pre-processing step for dealing with real value attributes. Moreover, attribute reduction is another key problem in rough set theory, and finding a minimal

Resolution of structure characteristics of passive acoustic emission signals in multiphase flow system

The underlying structure characteristics of acoustic emissions (AE) measured in a gas-solid fluidized bed was investigated detailedly by resorting to wavelet transform and rescaled range analysis. A general criterion was proposed to resolve AE signals into three characteristic scales, i.e. micro-, meso- and macro-scale, and a so-called structure diagram was introduced. Compared with the structure diagram of pressure signals, it was found that AE signals in micro-scale reflect mainly the particles motion while pressure signals in meso-scale reflect mainly the bubbles motion. Energy distribution analysis further revealed that the most energies in AE and pressure signals were distributed mainly by the micro-scale and meso-scale signals respectively. Moreover, the structure characteristics of AE signals collected from gas-solid fluidized bed and liquid-solid stirred tank were compared based on structure diagram and energy distribution analysis. The results indicated that although the same measurement technique was adopted, the structure characteristics of AE signals measured in gas-solid fluidized bed and liquid-solid stirred tank still exhibited larger difference. As an illustrative application of AE technique in process monitoring, a prediction model for particle size distribution was proposed and the satisfactory results were obtained both for laboratory scale and plant scale fluidized beds.