Granule Margin-Based Feature Selection in Weighted Neighborhood Systems.

Distance metric learning-based multi-granularity neighborhood rough sets for attribute reduction

Incremental feature selection for dynamic hybrid data using neighborhood rough set

Neighborhood rough set based heterogeneous feature subset selection

Cost-sensitive feature selection based on adaptive neighborhood granularity with multi-level confidence

Neighborhood margin rough set: Self-tuning neighborhood threshold

A novel approach to attribute reduction based on weighted neighborhood rough sets

Variable radius neighborhood rough sets and attribute reduction

Considering the fact that the early fault of the rolling-element bearing is difficult to identify correctly, a neighborhood rough set (NRS) and ensemble empirical mode decomposition (EEMD) fault diagnosis model of rolling-element bearing combination is come up with. First of all, the original signal of the vibration is decomposed by EEMD to obtain a number of IMF components, and extracted time-features from the first 3 IMF components in the time domain to form the original features. Then, NRS is used to reduce the attributes of original features, eliminate the redundant information, and put the sensitive reduced feature attributes into the SVM classifier for fault identification. This model is applied to typical rolling-element bearing fault diagnosis experiments, which shows that the NRS is used to select a large number of features containing abundant fault information from the original features. The method not only reduces the complexity of the classification algorithm, but also enhances the accuracy of the fault identification by 5%, which provides a new approach of analysis model for rolling-element bearing fault diagnosis.

Feature selection is one of the widely used pre-processing techniques to deal with large data sets. In this context, rough set theory has been successfully implemented for feature selection of discrete data set but in case of continuous data set it requires discretization, which may cause information loss. Fuzzy rough set theory approaches have also been used successfully to resolve this issue as it can handle continuous data directly. Moreover, almost all feature selection techniques are used to handle homogeneous data set. In this article, the center of attraction is on heterogeneous feature subset reduction. A novel intuitionistic fuzzy neighborhood models have been proposed by combining intuitionistic fuzzy sets and neighborhood rough set models by taking an appropriate pair of lower and upper approximations and generalize it for feature selection, supported with theory and its validation. An appropriate algorithm along with application to a data set has been added.

Neighborhood rough set theory is an important method for dealing with uncertainty, fuzziness and undefined objects, and neighborhood rough set theory based on binary relation is studied by scholars a lot. However, most of the current research is used to deal with equivalent binary relations or other binary relations with stringent condition, which has been unable to meet the rapid development of data. This paper proposes a new multi-granulation rough set that can deal with general binary relations based on the C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</inf> neighborhoods, and verifies its related properties. At the same −time, it has richer meanings through weighted methods. Theoretical analysis and practical examples show that this rough set has better ability to process data. Finally, a weighted attribute reduction algorithm is designed based on the significant function under the general binary relation. The experimental results show that this rough set can handle complex data well. The research in this paper develops the theory of classical rough sets, and provides a theoretical basis for the knowledge acquisition of information systems under the general binary relation.

In many big data applications, online streaming feature selection plays a critical role in processing feature stream and dealing with high-dimensional problems. However, traditional online streaming feature selection methods focus on relevant features, irrelevant and/or redundant features, ignore the interaction between features. i.e., individual feature and label are irrelevant or weakly correlated, but when it is combined with another irrelevant or weakly feature, they show strongly correlated with label. In this paper, we propose a novel feature selection algorithm that considers feature interaction based on neighborhood rough set. This algorithm select features based on the following principles: the discrimination capability of the selected feature subset should be greater than or equal to the original feature space, and the number of features subset should be as small as possible by using feature interaction. Under this framework, we propose an online significance analysis criterion to select significance features relative to the currently selected features, and design an online redundancy analysis criterion to retain highly interactive features and filter out redundant features. Experimental results on a series of benchmark datasets show that the proposed algorithm significantly outperforms other state-of-the-art online streaming feature selection methods.

Chapter 14 - Review on hybrid feature selection and classification of microarray gene expression data

Computer-aided diagnosis has the potential of increasing diagnostic accuracy by providing a second reading to radiologists. In many computerized schemes, numerous features can be extracted to describe suspect image regions. A subset of these features is then employed in a data classifier to determine whether the suspect region is abnormal or normal. Different subsets of features will, in general, result in different classification performances. A feature selection method is often used to determine an "optimal" subset of features to use with a particular classifier. A classifier performance measure (such as the area under the receiver operating characteristic curve) must be incorporated into this feature selection process. With limited datasets, however, there is a distribution in the classifier performance measure for a given classifier and subset of features. In this paper, we investigate the variation in the selected subset of "optimal" features as compared with the true optimal subset of features caused by this distribution of classifier performance. We consider examples in which the probability that the optimal subset of features is selected can be analytically computed. We show the dependence of this probability on the dataset sample size, the total number of features from which to select, the number of features selected, and the performance of the true optimal subset. Once a subset of features has been selected, the parameters of the data classifier must be determined. We show that, with limited datasets and/or a large number of features from which to choose, bias is introduced if the classifier parameters are determined using the same data that were employed to select the "optimal" subset of features.

Classification of omics data suffers from the high error rate due to their high dimensional and small sample size characteristics. To overcome the problem, this paper proposes an ensemble feature selection for omics data classification based on constrained niching binary particle swarm optimization (PSO). Particularly, optimal feature subsets in terms of best classification accuracy are identified by the binary PSO. The proposed method introduces constraint on the particle encoding to constrain the number of selected features, and niching technique from multimodal optimization is imposed to enable the algorithm to obtain multiple diverse feature subsets in a single run. Afterward, multiple base classifiers built on the obtained feature subsets are combined into a stronger classifier which is applied to classify the omics data. Experimental results on real-world omics datasets demonstrate that the proposed feature selection method can stably select compact feature subsets and obtain promising classification performance.

In many pattern recognition applications feature selection and instance selection can be used as two data preprocessing methods that aim at reducing the computational cost of the learning process. Moreover, in some cases, feature subset selection can improve the classification performance. Feature selection and instance selection can be interesting since the choice of features and instances greatly influence the performance of the learnt models as well as their training costs. In the past, unifying both problems was carried out by solving a global optimization problem using meta-heuristics. This paradigm not only does not exploit the manifold structure of data but can be computationally expensive. To the best of our knowledge, the joint use of sparse modeling representative and feature subset relevance have not been exploited by the joint feature and selection methods. In this paper, we target the joint feature and instance selection by adopting feature subset relevance and sparse modeling representative selection. More precisely, we propose three schemes for the joint feature and instance selection. The first is a wrapper technique while the two remaining ones are filter approaches. In the filter approaches, the search process adopts a genetic algorithm in which the evaluation is mainly given by a score that quantify the goodness of the features and instances. An efficient instance selection technique is used and integrated in the search process in order to adapt the instances to the candidate feature subset. We evaluate the performance of the proposed schemes using image classification where classifiers are the nearest neighbor classifier and support vector machine classifier. The study is conducted on five public image datasets. These experiments show the superiority of the proposed schemes over various baselines. The results confirm that the filter approaches leads to promising improvement on classification accuracy when both feature selection and instance selection are adopted.