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## International Journal of Machine Learning and Cybernetics OnlineFirst articles

24.01.2021 | Original Article

### A scalable network intrusion detection system towards detecting, discovering, and learning unknown attacks

Network intrusion detection systems (IDSs) based on deep learning have reached fairly accurate attack detection rates. But these deep learning approaches usually have been performed in a closed-set protocol that only known classes appear in …

Autoren:
Zhao Zhang, Yong Zhang, Da Guo, Mei Song

24.01.2021 | Original Article

### Deep reinforcement learning based home energy management system with devices operational dependencies

Advanced metering infrastructure and bilateral communication technologies facilitate the development of the home energy management system in the smart home. In this paper, we propose an energy management strategy for controllable loads based on …

Autoren:
Caomingzhe Si, Yuechuan Tao, Jing Qiu, Shuying Lai, Junhua Zhao

24.01.2021 | Original Article

### Feature distribution-based label correlation in multi-label classification

In multi-label classification, multiple label variables in output space are equally important and can be predicted according to a common set of input variables. To improve the accuracy and efficiency of multi-label learner, measuring and utilizing …

Autoren:
Xiaoya Che, Degang Chen, Jusheng Mi

24.01.2021 | Original Article

### q-ROF-SIR methods and their applications to multiple attribute decision making

q-rung orthopair fuzzy set (q-ROFS) is a useful tool to express uncertain information. With the parameter q increasing, q-ROFSs have broader space for describing uncertain information than intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy …

Autoren:
Hua Zhu, Jianbin Zhao, Hua Li

24.01.2021 | Original Article

### Problems selection under dynamic selection of the best base classifier in one versus one: PSEUDOVO

Class binarization techniques are used to decompose multi-class problems into several easier-to-solve binary sub-problems. One of the most popular binarization techniques is One versus One (OVO), which creates a sub-problem for each pair of …

Autoren:
Izaro Goienetxea, Iñigo Mendialdua, Igor Rodríguez, Basilio Sierra

24.01.2021 | Original Article

### Sample-based online learning for bi-regular hinge loss

Support vector machine (SVM), a state-of-the-art classifier for supervised classification task, is famous for its strong generalization guarantees derived from the max-margin property. In this paper, we focus on the maximum margin classification …

Autoren:
Wei Xue, Ping Zhong, Wensheng Zhang, Gaohang Yu, Yebin Chen

16.01.2021 | Original Article

### L-fuzzifying approximation operators derived from general L-fuzzifying neighborhood systems

For a completely distributive De Morgan algebra L, we develop a general framework of L-fuzzy rough sets. Said precisely, we introduce a pair of L-fuzzy approximation operators, called upper and lower L-fuzzifying approximation operators derived …

Autoren:
Lingqiang Li, Bingxue Yao, Jianming Zhan, Qiu Jin

14.01.2021 | Original Article

### A multiple-kernel clustering based intrusion detection scheme for 5G and IoT networks

The 5G network provides higher bandwidth and lower latency for edge IoT devices to access the core business network. But at the same time, it also expands the attack surface of the core network, which makes the enterprise network face greater …

Autoren:
Ning Hu, Zhihong Tian, Hui Lu, Xiaojiang Du, Mohsen Guizani

14.01.2021 | Original Article

### An automatic three-way clustering method based on sample similarity

The three-way clustering is an extension of traditional clustering by adding the concept of fringe region, which can effectively solve the problem of inaccurate decision-making caused by inaccurate information or insufficient data in traditional …

Autoren:
Xiuyi Jia, Ya Rao, Weiwei Li, Sichun Yang, Hong Yu

14.01.2021 | Original Article

### Knowledge granularity reduction for decision tables

Attribute reduction is a difficult topic in rough set theory and knowledge granularity reduction is one of the important types of reduction. However, up to now, its reduction algorithm based on a discernibility matrix has not been given. In this …

Autoren:
Guilong Liu, Yanbin Feng

14.01.2021 | Original Article

### -norm probabilistic K-means clustering via nonlinear programming

Generalized fuzzy c-means (GFCM) is an extension of fuzzy c-means using $$L_{p}$$ L p -norm distances. However, existing methods cannot solve GFCM with m = 1. To solve this problem, we define a new kind of clustering models, called $$L_{p}$$ L p …

Autoren:
Bowen Liu, Yujian Li, Ting Zhang, Zhaoying Liu

14.01.2021 | Original Article

### Hierarchical multi-attention networks for document classification

Research of document classification is ongoing to employ the attention based-deep learning algorithms and achieves impressive results. Owing to the complexity of the document, classical models, as well as single attention mechanism, fail to meet …

Autoren:
Yingren Huang, Jiaojiao Chen, Shaomin Zheng, Yun Xue, Xiaohui Hu

14.01.2021 | Original Article

### Consensus model based on probability K-means clustering algorithm for large scale group decision making

Nowadays, the increasing complexity of the social environment brings much difficulty in group decision making. The more uncertainty exists in a decision-making problem, the more collective wisdom is needed. Therefore, large scale group decision …

Autoren:
Qian Liu, Hangyao Wu, Zeshui Xu

14.01.2021 | Original Article

### Routing protocol for low power and lossy network–load balancing time-based

Recently 6G/IoT emerged the latest technology of traditional wireless sensor network devices for 6G/IoT-oriented infrastructure. The construction of 6G/IoT utilizes the routing protocol for low power and lossy networks (RPL) protocol in the …

Autoren:

14.01.2021 | Original Article

### Group decision making for internet public opinion emergency based upon linguistic intuitionistic fuzzy information

With the wide use of network, the outbreak of network public opinion emergencies has changed from single to multiple. The goal of the current study is to construct the emergency group decision-making (EGDM) model for multiple network public …

Autoren:
Yi Liu, Guiwu Wei, Haobin Liu, Lei Xu

14.01.2021 | Original Article

### Joint learning of author and citation contexts for computing drift in scholarly documents

Scholarly documents are sources of information on research topics written by academic experts. Topic drift in such scholarly documents is usually linked with the contextual variation in the title or abstract or entire document over time. However …

Autoren:
J. Vijayarani, T. V. Geetha

12.01.2021 | Original Article

### Generating transferable adversarial examples based on perceptually-aligned perturbation

Neural networks (NNs) are known to be susceptible to adversarial examples (AEs), which are intentionally designed to deceive a target classifier by adding small perturbations to the inputs. And interestingly, AEs crafted for one NN can mislead …

Autoren:
Hongqiao Chen, Keda Lu, Xianmin Wang, Jin Li

10.01.2021 | Original Article

### Clone detection in 5G-enabled social IoT system using graph semantics and deep learning model

The protection and privacy of the 5G-IoT framework is a major challenge due to the vast number of mobile devices. Specialized applications running these 5G-IoT systems may be vulnerable to clone attacks. Cloning applications can be achieved by …

Autoren:
Farhan Ullah, Muhammad Rashid Naeem, Leonardo Mostarda, Syed Aziz Shah

05.01.2021 | Original Article

### Dynamic neural orthogonal mapping for fault detection

Dynamic principal component analysis (DPCA) and its nonlinear extension, dynamic kernel principal component analysis (DKPCA), are widely used in the monitoring of dynamic multivariate processes. In traditional DPCA and DKPCA, extended vectors …

Autoren:
Zhengwei Hu, Jingchao Peng, Haitao Zhao

04.01.2021 | Original Article

### Regularized based implicit Lagrangian twin extreme learning machine in primal for pattern classification

In this paper, we suggest a novel approach termed as regularized based implicit Lagrangian twin extreme learning machine in primal as a pair of unconstrained convex minimization problem (RILTELM) where regularization term is added to follow the …

Autoren:
Umesh Gupta, Deepak Gupta