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Adversarial examples are augmented data points generated by imperceptible perturbation of input samples. They have recently drawn much attention with the machine learning and data mining community. Being difficult to distinguish from real examples, such adversarial examples could change the prediction of many of the best learning models including the state-of-the-art deep learning models. Recent attempts...
Various lower and upper approximations defined by the tolerance classes of objects and maximal tolerance classes are compared in the tolerance rough set models. Based on the study, some suggestions for choosing the suitable lower and upper approximations are given for the purpose of improving approximation accuracy.
Recently, several algorithms based on the MapReduce framework have been proposed for frequent pattern mining in Big Data. However, the proposed solutions come with their own technical challenges, such as inter-communication costs, in-process synchronizations, balanced data distribution and input parameters tuning, which negatively affect the computation time. In this paper we present MrAdam, a novel...
The abundance and value of mining large time series data sets has long been acknowledged. Ubiquitous in fields ranging from astronomy, biology and web science the size and number of these datasets continues to increase, a situation exacerbated by the exponential growth of our digital footprints. The prevalence and potential utility of this data has led to a vast number of time-series data mining techniques,...
As graph-structured data sets become commonplace, there is increasing need for efficient ways of analyzing such data sets. These analyses include conservation, alignment, differentiation, and discrimination, among others. When defined on general graphs, these problems are considerably harder than their well-studied counterparts on sets and sequences. In this paper, we study the problem of global alignment...
In this paper we propose the optimization of Rough Set method using ant colony for oil-impregnated paper bushings. Ant colony is used to discretize the training data set. The ant colony optimized rough set is compare to a rough set who's data is discretized using equal frequency bin (EFB). Ant colony optimized (ACO) rough set results show an improvement compared to the EFB. The ACO rough set has an...
The classification of imbalanced data is a well-studied topic in data mining. However, there is still a lack of understanding of the factors that make the problem difficult. In this work, we study the two main reasons that make the classification of imbalanced datasets complex: overlapping and data fracture. We present a Genetic Programming-based feature extraction method driven by Rough Set Theory...
Fuzzy C-means (FCM) and Rough K-means (RKM) algorithms are two popular soft clustering algorithms that allow for overlapping clusters. The overlapping clusters can be useful in applications where restrictions imposed by crisp clustering that force assignment of every object to a unique cluster may not be practical. Likewise RKM and FCM, interval set representation of clusters would also generate overlapping...
Rough sets theory can be used to research imprecise and incomplete problems in information systems. Conflict analysis and resolution play an important role in business, governmental, political and lawsuits disputes, labor-management negotiations, military operations and others. This article illustrates the proposed approach by means of a simple tutorial example of voting analysis in conflict situations...
This paper presents a method for the fast and direct extraction of model parameters for capacitive MEMS resonators from their measured transmission response such as quality factor, resonant frequency, and motional resistance. We show that these parameters may be extracted without having to first de-embed the resonator motional current from the feedthrough. The series and parallel resonances from the...
Grade is an important quantitative index, and graded rough set model is an important improved rough set model. The purpose of this paper is to explore product operation of grade approximation operators. Based on logical product operation of grade approximation operators, this paper proposes product operation of grade upper approximation operator and grade lower approximation operator based on two...
In this paper, we show that frame coefficients related to Riesz bases of integer-translates are solutions of bi-infinite invertible Toeplitz systems. Hence the corresponding frame coefficients may be obtained by solving Toeplitz system directly, without complicated calculation of the inverse frame operator. We apply the finite section method to Toeplitz operator and provide a bi-directional Levinson...
In this paper, a large class of parabolic inverse problem is transformed into a nonclassical parabolic equation whose coefficients consist of trace type functional of the solution and its derivatives are subject to some initial and boundary conditions. This nonclassical problem is approximated numerically by the finite element method, and the optimal convergence rate of order O(hr+1) is obtained for...
The data in many disciplines such as social networks, web analysis, etc. is link-based, and the link structure can be exploited for many different data mining tasks. In this paper, we consider the problem of temporal link prediction: Given link data for time periods 1 through T, can we predict the links in time period T + 1? Specifically, we look at bipartite graphs changing over time and consider...
Similarity calculation has many applications, such as information retrieval, and collaborative filtering, among many others. It has been shown that link-based similarity measure, such as SimRank, is very effective in characterizing the object similarities in networks, such as the Web, by exploiting the object-to-object relationship. Unfortunately, it is prohibitively expensive to compute the link-based...
Fast changing knowledge on the Internet can be acquired more efficiently with the help of automatic document summarization and updating techniques. This paper described a novel approach for multi-document update summarization. The best summary is defined to be the one which has the minimum information distance to the entire document set. The best update summary has the minimum conditional information...
This paper introduces a new modeling framework for failure analysis (FA) of physical systems namely ??Rate cognitive maps??. The proposed cognitive map based framework provides a robust approach for identifying a large variety of possible failure modes in a dynamic environment through a new cognitive inference process. While this methodology allows the identification of possible failure modes at multiple...
With the application and popularization of autonomic computing in the field of aerospace exploration, large-scale database management and critical network control, existing self-reflection models based on natural language or diagram can not meet the requirements of analysis and verification. In this article, two kinds of self-reflection models, one is the static and the other is dynamic, capable of...
We consider the problem of analysis and control of spatially invariant discretely distributed systems. It is well known that for certain types of subsystem models, the interconnected systems can be represented by infinite dimensional Laurent operators with rational symbols. Using Fourier techniques, the resulting analysis and control problems can be written as finite dimensional eigenvalue inequalities,...
The nonlinear filtering problem is considered for the time homogeneous diffusion model with correlated noise. A numerical approach is proposed for computing approximations of the unnormalized filtering density (UFD) and the nonlinear filtering. This approach is based on the Wiener chaos expansion (WCE) of the solution of Zakai equation. A Sparse truncation method of WCE is introduced to simplify calculation,...
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