Granular Classifier Research Articles

Granular classifier: Building traffic granules for encrypted traffic classification based on granular computing

Accurate classification of encrypted traffic plays an important role in network management. However, current methods confronts several problems: inability to characterize traffic that exhibits great dispersion, inability to classify traffic with multi-level features, and degradation due to limited training traffic size. To address these problems, this paper proposes a traffic granularity-based cryptographic traffic classification method, called Granular Classifier (GC). In this paper, a novel Cardinality-based Constrained Fuzzy C-Means (CCFCM) clustering algorithm is proposed to address the problem caused by limited training traffic, considering the ratio of cardinality that must be linked between flows to achieve good traffic partitioning. Then, an original representation format of traffic is presented based on granular computing, named Traffic Granules (TG), to accurately describe traffic structure by catching the dispersion of different traffic features. Each granule is a compact set of similar data with a refined boundary by excluding outliers. Based on TG, GC is constructed to perform traffic classification based on multi-level features. The performance of the GC is evaluated based on real-world encrypted network traffic data. Experimental results show that the GC achieves outstanding performance for encrypted traffic classification with limited size of training traffic and keeps accurate classification in dynamic network conditions.

Fuzzy granular convolutional classifiers

Convolutional operations extracting effective features have been widely used in the field of deep learning. For the convolution is difficult to process set data, we propose two convolutional operators on fuzzy sets, and build a fuzzy granular classifier. Firstly, a fuzzy granulation is performed on single-atom features of classification systems to form fuzzy conditional granules and fuzzy decision granules. Then, a fuzzy conditional granular vector is constructed from the fuzzy conditional granules, and a convolutional operation is carried out on the granular vector. After that, a new fuzzy feature granule is obtained. The fuzzy feature granule is compared with its corresponding fuzzy decision granule. The result of comparison is back-propagated to the fuzzy granular vector. Simultaneously, weights of the fuzzy granular vector are modified. Thus, a fuzzy granular convolutional classifier is formed by iterating and optimizing the weights of fuzzy granular vectors several times. Furthermore, we prove the difference and derivative of fuzzy granular convolution, which provide a theoretical basis for the back-propagation of the fuzzy granular convolutional classifier. Finally, the convergence effects of the fuzzy granular convolutional operations and the classification performance of the proposed classifier are tested on some UCI datasets. The theoretical analysis and experimental results show that the convolutional operations of fuzzy granular vectors have the characteristics of fast convergence, and the fuzzy granular convolutional classifier obtains a better classification performance.

On a granular functional link network for classification

In this paper, we present a new granular classifier in two versions (iterative and non–iterative), by adopting some ideas originating from a kind of Functional Link Artificial Neural Network and the Functional Network schemes. These two architectures are substantially the same: they both use a function basis instead of the usual activation function, but they are different for the learning algorithm. We augment them from the perspective of Granular Computing and information granules, designing a new kind of classifier and two learning algorithms, by taking into account granularity of information. The proposed classifier exhibits the advantages of the granular architectures, that is higher accuracy and transparency. We formally discuss the convergence of the iterative learning scheme. We carry out some numerical experiments using publicly available data, by comparing the results against those results produced by the state-of-the-art methods. In particular, we achieved sound results by invoking the iterative learning scheme.

Entropy clustering-based granular classifiers for network intrusion detection

Support vector machine (SVM) is one of the effective classifiers in the field of network intrusion detection; however, some important information related to classification might be lost in the reprocessing. In this paper, we propose a granular classifier based on entropy clustering method and support vector machine to overcome this limitation. The overall design of classifier is realized with the aid of if-then rules that consists of a premise part and conclusion part. The premise part realized by the entropy clustering method is used here to address the problem of a possible curse of dimensionality, while the conclusion part realized by support vector machines is utilized to build local models. In contrast to the conventional SVM, the proposed entropy clustering-based granular classifiers (ECGC) can be regarded as an entropy-based support function machine. Moreover, an opposition-based genetic algorithm is proposed to optimize the design parameters of the granular classifiers. Experimental results show the effectiveness of the ECGC when compared with some classical models reported in the literatures.

Granule Vectors and Granular Convolutional Classifiers

Convolutional operations can extract effective features and have been widely used in the field of deep learning. For the deficiency of convolution mainly dealing with numerical data, we propose a novel convolutional operator on granules with a set form, further we build a classifier on it. Firstly, feature granules are constructed on each single feature of a classification system by introducing neighborhood rough sets. Synchronously, decision granules are generated on the labels of samples. Secondly, feature granule vectors and weighted granule vectors are constructed from these granules, and a convolutional operation is proposed on feature granule vectors and weighted granule vectors, then a predicted granule is produced as a result of the convolutional operation. The predicted granule is compared with the decision granule, and their residual error is back propagated to the weighted granule vector for tuning its value. After multiple iterations of the granular convolutional operations and back propagation corrections, the weight of the granular vector is convergent and optimized. Furthermore, a granular classifier is designed based on the convolutional operation. The constringency of the granular convolution and the classification performance of the granular classifier are tested on some UCI datasets. Theoretical analysis and experimental results show that the granular convolution has a characteristic of fast convergence, and the granular convolutional classifier has a better classification performance.

Granular ball computing classifiers for efficient, scalable and robust learning

Granular computing is an efficient and scalable computing method. Most of the existing granular computing-based classifiers treat the granules as a preliminary feature procession method, without revising the mathematical model and improving the main performance of the classifiers themselves. So far, only few methods, such as the G-svm and WLMSVM, have been combined with specific classifiers. Because of the complete symmetry of the ball and its simple mathematical expression, it is relatively easy to be combined with the other classifiers’ mathematical models. Therefore, this paper uses a ball to represent the grain, namely the granular ball, and not only the granular balls’ labels but also the distance between a pair of balls is defined. Based on that, this paper attempts to propose a new granular classifier framework by replacing the point inputs with the granular balls. We derive the basic model of both the granular ball support vector machine and granular ball k-nearest neighbor algorithm (GBkNN). In addition, the GBkNN is compared with the k-means tree based kNN, which is the most efficient and effective kNN as far as we known, on both public and artificial data sets. The Experimental results demonstrate the effectiveness and efficiency of the proposed framework.

Boosting Effect of Classifier Based on Simple Granules of Knowledge*

. (THIS IS THE FIRST VERSION, I WILL PROVIDE A CORRECTION IN A FEW DAYS TIME, AND THE RESULTS WILL BE DESCRIBED IN DETAIL)The idea of classification based on simple granules of knowledge (CSG classifier) is inspired by granular structures proposed by Polkowski. The simple granular classifier turned up to be really effective in the context of real data classification. Classifier among others turned out to be resistant for damages and can absorb missing values. In this work we have presented the continuation of series of experimentations with boosting of rough set classifiers. In this work we have checked a few methods for classifier stabilization in the context of CSG classifier - Bootstrap Ensemble (Simple Bagging), Boosting based on Arcing, and Ada-Boost with Monte Carlo split. We have performed experiments on selected data from the UCI Repository. For the smaller radii we have huge unprecise classificaiton granules, and the best result was obtain for the first radius after 0.5, where the size of classification granules was about 4,5 percent of size of original decision system. For the higher radii starting from 0.642857 classification granule size is really small and the classification is worse. *extended version of paper „Ensemble of Classifiers Based on Simple Granules of Knowledge” presented at the 23rd International Conference on Information and Software Technologies (ICIST 2017) held on 12-14 October, 2017 in Druskininkai, LithuaniaDOI: http://dx.doi.org/10.5755/j01.itc.47.2.19675

Fuzzy granular classifier approach for spam detection

Spam email problem is a major shortcoming of email technology for computer security. In this research, a granular classifier model is proposed to discover hyperboxes in the geometry of information granules for spam detection in three steps. In the first step, the k-means clustering algorithm is applied to find the seed_points to build the granular structure of the spam and non-spam patterns. Moreover, applying the interval analysis through the high homogeneity of the patterns captures the key part of the spam and non-spam classifiers’ structure. In the second step, PSO algorithm is hybridized with the k-means to optimize the formalized information granules’ performance. The proposed model is evaluated based on the accuracy, misclassification and coverage criteria. Experimental results reveal that the performance of our proposed model is increased through applying Particle Swarm Optimization and fuzzy set.

A Granular Classifier By Means of Context-based Similarity Clustering

In this study, we propose a granular classifier (GC) with the aid of a context-based similarity clustering (CSC) method and applied it for network intrusion detection. The proposed CSC supporting the design of information granules is exploited here to determine the so-called contexts. Unlike the conventional similar clustering method, here the CSC built clusters by taking into consideration of both input data and output data. The design of granular classifier is realized based on the if-then rules, which consists two parts: namely premise part and conclusion part. The premise part is developed by using the CSC, while the conclusion part is realized with the aid of supported vector machines. In contrast to typical rule-based classifier, the underlying principle exploited here is to consider a robust classification with the adequate use of output data. In particular, rule-based classifiers or supported vector machines can be regarded as a special case of the proposed granular classifier. Numeric studies show the superiority of the proposed approach.

Granular classifiers and their design through refinement of information granules

In this study, we focus on the design and refinements of granular pattern classifiers, namely classifiers, which deal with a collection of information granules formed in a certain feature space. The development of this category of classifiers is realized as a two-phase design process. First, information granules occupying some regions of the feature space are formed through invoking mechanisms of clustering or fuzzy clustering. As a result, regions in the feature space are built, which are densely occupied by the patterns predominantly belonging to the same class. We offer a detailed way of assessing the character and quality of information granules and their information (classification-oriented) content. The resulting description is utilized in the realization of the classification mechanism being considered at the second phase of the design of the granular classifier. The mapping from the collection of information granules to class assignment (classification) involves matching of a pattern to be classified to individual information granules and aggregating them by considering the information content of the corresponding granules. In the study, a number of descriptors capturing information content and aggregation functions are analyzed. To improve the performance of the granular classifier, a refinement of information granules is carried out, in which highly heterogeneous information granules (viz. those containing patterns belonging to various classes) are refined (split, specialized), and their refined versions are afterwards used in the buildup of the classifier. A series of experiments involving both synthetic data as well as those publicly available is reported and analyzed, illustrating the main advantages of granular classifiers and their design procedure.

The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier

Granulation extracts a bundle of similar patterns by decomposing universe. Hyperboxes are granular classifiers to confront the uncertainties in granular computing. This paper proposes a granular classifier to discover hyperboxes in three phases. The first phase of the proposed model uses the set calculus to build the hyperboxes; where, the means of the DBSCAN clustering algorithm constructs the structure. The second phase develops the geometry of hyperboxes to improve the classification rate. It uses the Particle Swarm Optimization (PSO) algorithm to optimize the seed_points and expand the hyperboxes. Finally, the third phase identifies the noise points; where, the patterns in the second phase did not belong to any hyperboxes. We have used the capability of membership function of a fuzzy set to improve the geometry of classifier. The performance of a proposed model is carried out in terms of coverage, misclassification error and accuracy. Experimental results reveal that the proposed model can adaptively choose an appropriate granularity.

Description and classification of granular time series

The study is concerned with a concept and a design of granular time series and granular classifiers. In contrast to the plethora of various models of time series, which are predominantly numeric, we propose to effectively exploit the idea of information granules in the description and classification of time series. The numeric (optimization-oriented) and interpretation abilities of granular time series and their classifiers are highlighted and quantified. A general topology of the granular classifier involving a formation of a granular feature space and the usage of the framework of relational structures (relational equations) in the realization of the classifiers is presented. A detailed design process is elaborated on along with a discussion of the pertinent optimization mechanisms. A series of experiments is covered leading to a quantitative assessment of the granular classifiers and their parametric analysis.

The design of granular classifiers: A study in the synergy of interval calculus and fuzzy sets in pattern recognition

In this study, we are concerned with a development of a certain category of granular classifiers referred here to as hyperbox-driven classifiers (HDC). The approach fully capitalizes on the two key technologies of granular computing, namely set (interval) calculus and fuzzy sets in dealing with a description of geometry of patterns (data) belonging to a certain category. We take advantage of the capabilities of sets (intervals and their Cartesian products) when describing a core structure of classes of patterns in the form of some hyperboxes. Their combinations are referred to as a core structure of the feature space. Next, we refine the geometry of the classifier by bringing forward the concepts of regions of the feature space characterized by fuzzy sets. They are sought as a secondary structure. The construction of the core structure is realized by means of the supervised version of DBSCAN—one of the popular clustering algorithms of data mining. The secondary structure is described by fuzzy sets whose membership functions are a solution to some optimization problem of allocation of degrees of belongingness. In the formation of the secondary structure we exploit a concept of the Hausdorff distance that determines a distance between some information granule (of a well defined geometry) and a numeric pattern (a point in the highly dimensional feature space). The two-stage description of the granular classifier is also beneficial in the characterization of the classification error. By virtue of the design of the core structure, the classification error occurring there assumes very small, almost zero values. On the other hand, the secondary structure coming with a substantial level of overlap between classes is characterized by relatively higher values of the classification error. Being cognizant of that, we suggest a way of forming a synthetic measure built on the membership values and named a separability index that helps quantify the classification error in this setting. A series of numeric examples are used to demonstrate the effectiveness of the proposed granular classifiers.