Comparison of three rough surface classifiers

A Comparison of Three Rough Surface Classifiers

In this paper texture analysis techniques are used to segment rough surfaces into regions of homogeneous texture. The performance of three rough surface classifiers was assessed and compared. The classifiers differ in their discrimination as well as their input and computational requirements. Experiments were used to identify the failure modes of the classifiers and to identify which classifier...

Rough Classifiers

Hierarchical Rough Classifiers

The major applications of rough set theory in data mining are related to the modeling of concepts using rough classifiers, i.e., the algorithms classifying unseen objects into lower or upper approximations of concepts. This paper investigates a class of compound classifiers called multi-level (or hierarchical) rough classifiers (MLRC). We present the most recent issues on the construction of su...

Robust fuzzy rough classifiers

Fuzzy rough sets, generalized from Pawlak’s rough sets, were introduced for dealing with continuous or fuzzy data. This model has been widely discussed and applied these years. It is shown that the model of fuzzy rough sets is sensitive to noisy samples, especially sensitive to mislabeled samples. As data are usually contaminated with noise in practice, a robust model is desirable. We introduce...

Building Scalable Lazy Rough Classifiers

We present a lazy classification method, which is equivalent to the rough set based method for construction of classifiers, but is scalable and applicable for large data sets. The proposed method is based on our previous papers Nguyen (2002) Nguyen (2006), where the relationship between rough sets methods and association learning methods were discovered. In this method the set of decision rules...