International Journal of Collaborative Intelligence (8 papers in press)
NNGDPC: a kNNG-based Density Peaks Clustering
by Miao Li
Abstract: Density peaks clustering (DPC) algorithm is a novel clustering algorithm based on density. It needs neither iterative process nor more parameters. However, the geometry of the distribution of the data has not been taken into account in the original algorithm. DPC does not perform well when clusters have different densities. In order to overcome this problem, we propose a novel density peaks clustering based on k-nearest neighbor graph called NNGDPC (kNNG-based Density Peaks Clustering) which introduces the idea of the nearest neighbors (k-NNG) into DPC. By experiments on synthetic data sets, we show the power of the proposed algorithm. Experimental results show that our algorithm is feasible and effective.
Keywords: Data clustering; Density peaks; K-nearest neighbor graph.
Density-based Multi-weight vector support vector machine
by Xiaopeng Hua
Abstract: Recently proposed Muti-weight vector support vector machine (MVSVM) considers all of points and views them as equally important points. In real cases, most of the points of a dataset are highly correlated, at least locally, or the dataset has an inherent geometrical property. These points generally lie in the high density regions and are crucial for data classification. This motivates the rush toward new classifiers that can sufficiently take advantage of the points in the high density regions. In this paper, a novel binary classifier called density-based multi-weight vector support vector machine (DMVSVM) is presented. With the introduction of underlying correlated information between points, DMVSVM not only inherits the merit of MVSVM, but also has its additional characteristics: (1) density weighting method is adopted to measure the importance of points in the same class; (2) having comparable or better classification ability compared to MVSVM. The experimental results on publicly available datasets confirm the effectiveness of our method.
Keywords: multi-weight vector support vector machine; density; correlated information; classification.
Attribute Reduction Algorithm of Variable Neighborhood Rough Set Model Based on FCM
by Xinghui Zhao, Jiancong Fan, Yixuan Long
Abstract: Based on the analysis of existing neighborhood rough sets algorithm, a new attribute reduction algorithm, called Canopy-FCM-VNRSMAR algorithm by reducing attribute using Canopy-FCM variable neighborhood rough set model is proposed in this paper. This algorithm is constructed by using attribute importance degree as the heuristic condition and makes the setting of neighborhood value completely according to the distribution of data. So it avoids the disadvantages of setting the global neighborhood value. The experimental results on open datasets on UCI show that the proposed algorithm can preserve fewer conditional attributes and improve the classification accuracy of data. In addition, it can extend the use of neighborhood rough sets.
Keywords: Neighborhood Rough Set; Unsymmetrical Variable Neighborhood; Attribute Importance Degree; Global Fixed Neighborhood.
A Feature Weighted Affinity Propagation Clustering Algorithm Based on Rough Entropy Reduction
by X.U. LI
Abstract: In the clustering task, each feature of data sample is not taken the same contribution, some features provides more related information to the final results, if they are treated equally with other features, not only the complexity of the algorithm is increased but also the accuracy of the final results will be affected. So as a key phase in clustering, feature weighting is becoming more and more concerned by scholars. This paper proposes a feature weighted affinity propagation clustering algorithm based on rough entropy reduction (FWRER-AP). Rough entropy is used to assign weights for every feature according to their different contribution. Then the optimization samples are used in AP clustering algorithm, we can get the final clustering results through iterations. Compared with traditional AP clustering algorithm, experiment shows that the optimal algorithm not only reduces the complexity，but also improves the accuracy at the same time.
Keywords: rough entropy; attribute reduction; feature weighted; normalization; AP clustering.
A novel least squares twin parametric insensitive support vector regression
by Xiuxi Wei
Abstract: The recently proposed twin parametric insensitive support vector regression, denoted by TPISVR, gets perforce regression performance and is suitable for many cases, especially when the noise is heteroscedastic. However, in the TPISVR, it solves two dual quadratic programming problems (QPPs). Moreover, compared with support vector regression (SVR), TPISVR has at least four regularization parameters that need regulating, which would affect its practical applications. In this paper, we increase the efficiency of TPISVR from two aspects. Fist, by introducing the least squares method, we propose a novel least squares twin parametric insensitive support vector regression, called LSTPISVR for short. LSTPISVR attempts to solve two modified primal problems of TPISVR, instead of two dual problems usually solved. Compared with the traditional solution method, LSTPISVR can improve the training speed without loss of generalization. Second, a discrete binary particle swarm optimization (BPSO) algorithm is introduced to do the parameter selection. Computational results on several synthetic as well as benchmark datasets confirm the great improvements on the training process of our LSTPISVR.
Keywords: Support vector regression; Twin support vector regression; Least squares; twin parametric insensitive support vector regression; BPSO.
CCS Architectonic Design Refactoring as a potential solution to alienate the AE and Architectural scope creep A Case Study
by Manoj Kumar M
Abstract: Our earlier work (Manu A R, et. al, 2013, ManojKumar M et. al, 2014, V K Agrawal et. al, 2016, Nandakumar A.N et. al, 2012,) discussed the cloud structures and various security issues in crowd sourced multilateral cloud computing system such as architectural entropy, architectural smells, test debt, security debt, AD decay, AD degeneracy and technical debt etc. The discussion made in the earlier works [6-8] revealed that there is an inevitable demand for the identification of cloud security smells and various associated tasks so as to enable fallow-less and faultless cloud system design. Understanding stakeholders requirements and various mistakes made during the process design and optimization can be done where the focus can be made on alleviating the problems raised during transaction process. In this work a snippet of refactoring concept is provided. However, realizing the efficacy of refactoring towards Architecture entropy (AE) identification and removal in this work, a detailed discussion of refactoring and its implementation is presented. Realizing the probability of architectural entropy and smells, and it relation to architectural scope creep resulting adversaries particularly in terms of security breaches, in this work, with this phase it is intended to exploit the concept of refactoring and testing to assess architectural entropy and alleviate the same.
Keywords: cloud computing Architecture entropy; cloud computing architecture degradation; cloud computing architecture erosion; cloud computing architecture decay; cloud computing architecture depravation;.
A note on Restriected Boltzmann Machines and Variational AutoEncoders
by Jian Zhang
Abstract: This paper mainly introduces the theory and the abilities of generating images of Restricted Boltzmann Machines (RBMs) and Variational AutoEncoders (VAEs). Firstly, we introduce these models, which can be treated as the basic blocks of deep neural nets. Secondly, this paper introduces the ability of generating images based on these models. Finally, this paper introduces the hybrid model based on RBMs and VAEs and another model called a Boltzmann Encoded Adversarial Machine (BEAM). The experimental part shows the effectiveness of the hybrid models.
Keywords: Restricted Boltzmann Machines; Variational AutoEncoders; Generative Adversarial Nets; Deep Neural Nets.
Point-wise Gated Restricted Boltzmann Machines using Clean Data
by Nan Zhang
Abstract: The Point-wise Gated Restricted Boltzmann Machine (pgRBM), an RBM variant, can effectively find the task-relevant patterns from data containing irrelevant patterns and thus achieves satisfied classification results. Given that train data is compose of noisy data and clean data, how the clean data is applied to promote the performance of the pgRBM is a problem. To address the problem, this paper proposes a method, named as pgRBM based on Random Noisy Data and Clean Data (pgrncRBM). The pgrncRBM makes use of RBM and the clean data to obtain the initial values of the task-relevant weights, so it can learn the clean data from the data containing random noisy. In the pgrncRBM, the general RBM is used to pre-train the weights of task-relevant patterns from data and irrelevant patterns. If the noise is an image, the pgrncRBM cannot learn the task-relevant patterns from the noisy data. Spike-and-Slab RBM, an RBM variant, uses two types of hidden layers to determine the mean and covariance of each visible units. Therefore, this paper combines ssRBM with pgRBM and proposes a method, named as pgRBM based on Image Noisy Data and Clean Data (pgincRBM). The pgincRBM uses the ssRBM to model the noise, so it can learn the clean data from the data containing image noisy. And then, this paper stacks pgrncRBM, pgincRBM and RBMs to create deep networks. Experimental results on MNIST variation datasets show that pgrncRBM and pgincRBM are effective neural networks learning methods.
Keywords: Restricted Boltzmann Machine; Deep Belief Network; Feature Selection; Unsupervised Learning;.