A new heuristic possibilistic clustering algorithm for feature selection

Multiaspect Text Categorization Problem Solving: A Nearest Neighbours Classifier Based Approaches and Beyond

“…Concerning the simpli ication of the kNN.avg1 algorithm which we have considered, our experiments seem to show statistically signi icant reduction of the quality of the classi ication due to its use for most of the parameters settings, i.e., when the pairs of algorithms (4,6), (10,11), (14,15) and (18,19) in Table 6 are compared. However, for the setting where both techniques perform the best, i.e., when the parameter k is dynamically tuned (pair (3,5)), there is no statistically signi icant difference between the original kNN.avg1 technique and its simpli ied version.…”

“…Then we propose to model the cases and proceed with the classi ication of the documents in the frame-work of the hidden Markov models and sequence mining [22], using the concepts of the computational intelligence [23], or employing the support vector machines [24]. We also pursued other paths, including semantic representation of documents, inding a parallel of the MTC with text segmentation, studying the asymmetry of similarity [13,14], devising new cluster analysis techniques [11] or investigating the applicability of the concepts related to the coreference detection in data schemas [17].…”

Using Reinforcement Learning to Select an Optimal Feature Set

“…Second, it involves the machine learning algorithm (Decision tree) to evaluate pairs of features. In [32,33], a wellknown wrapper approach is presented, Recursive Feature Elimination using Random Forest (RFE). RFE performs feature selection recursively.…”

A Rapid Soft Computing Approach to Dimensionality Reduction in Model Construction

“…However, for the sake of consistency, our goodness criteria for the outputs are those of PCA and FA. In this respect fuzzy mountain clustering [5,32] and fuzzy c-means clustering methods [2,3,6,8,12,15,16,17,20,25,26] are analogous to PCA and FA, respectively.…”