Abstract Neighborhood classifiers

K nearest neighbor classifier (K-NN) is widely discussed and applied in pattern recognition and machine learning, however, as a similar lazy classifier using local information for recognizing a new test, neighborhood classifier, few literatures are reported on. In this paper, we introduce neighborhood rough set model as a uniform framework to understand and implement neighborhood classifiers. This algorithm integrates attribute reduction technique with classification learning. We study the influence of the three norms on attribute reduction and classification, and compare neighborhood classifier with KNN, CART and SVM. The experimental results show that neighborhood-based feature selection algorithm is able to delete most of the redundant and irrelevant features. The classification accuracies based on neighborhood classifier is superior to K-NN, CART in original feature spaces and reduced feature subspaces, and a little weaker than SVM

EROS: Ensemble rough subspaces

Ensemble learning is attracting much attention from pattern recognition and machine learning domains for good generalization. Both theoretical and experimental researches show that combining a set of accurate and diverse classifiers will lead to a powerful classification system. An algorithm, called FS-PP-EROS, for selective ensemble of rough subspaces is proposed in this paper. Rough set-based attribute reduction is introduced to generate a set of reducts, and then each reduct is used to train a base classifier. We introduce an accuracy-guided forward search and post-pruning strategy to select part of the base classifiers for constructing an efficient and effective ensemble system. The experiments show that classification accuracies of ensemble systems with accuracy-guided forward search strategy will increase at first, arrive at a maximal value, then decrease in sequentially adding the base classifiers. We delete the base classifiers added after the maximal accuracy. The experimental results show that the proposed ensemble systems outperform bagging and random subspace methods in terms of accuracy and size of ensemble systems. FS-PP-EROS can keep or improve the classification accuracy with very few base classifiers, which leads to a powerful and compact classification system

Consistency based attribute reduction

Rough sets are widely used in feature subset selection and attribute reduction. In most of the existing algorithms, the dependency function is employed to evaluate the quality of a feature subset. The disadvantages of using dependency are discussed in this paper. And the problem of forward greedy search algorithm based on dependency is presented. We introduce the consistency measure to deal with the problems. The relationship between dependency and consistency is analyzed. It is shown that consistency measure can reflects not only the size of decision positive region, like dependency, but also the sample distribution in the boundary region. Therefore it can more finely describe the distinguishing power of an attribute set. Based on consistency, we redefine the redundancy and reduct of a decision system. We construct a forward greedy search algorithm to find reducts based on consistency. What’s more, we employ cross validation to test the selected features, and reduce the overfitting features in a reduct. The experimental results with UCI data show that the proposed algorithm is effective and efficient.

on fuzzy-rough techniques

Information-preserving hybrid data reduction base

Interfacial engineering of ferromagnetism in wafer-scale van der Waals Fe4GeTe2 far above room temperature

Abstract Despite recent advances in exfoliated vdW ferromagnets, the widespread application of 2D magnetism requires a Curie temperature (Tc) above room temperature as well as a stable and controllable magnetic anisotropy. Here we demonstrate a large-scale iron-based vdW material Fe4GeTe2 with the Tc reaching ~530 K. We confirmed the high-temperature ferromagnetism by multiple characterizations. Theoretical calculations suggested that the interface-induced right shift of the localized states for unpaired Fe d electrons is the reason for the enhanced Tc, which was confirmed by ultraviolet photoelectron spectroscopy. Moreover, by precisely tailoring Fe concentration we achieved arbitrary control of magnetic anisotropy between out-of-plane and in-plane without inducing any phase disorders. Our finding sheds light on the high potential of Fe4GeTe2 in spintronics, which may open opportunities for room-temperature application of all-vdW spintronic devices