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Clustering is one of the most important techniques for unsupervised learning, it tries to divide points into different clusters without any priori knowledge of data. Therefore, several criterions for clustering algorithm are as follows: 1. ...

• A novel strategy was applied to estimate density and detect skeletal structure.
• Connectivity and density fluctuation were considered to assign skeletal points.
• The applied two strategies contain both global and local information of ...

Imbalanced data classification problem is a prevalent concern within the realms of machine learning and data mining. However, conventional methods primarily concentrate on between-class imbalance, ignoring noisy, overlap and within-class issues. ...

At present, 3D reconstruction technology is developing rapidly and is widely used in the field of medical education. Many experiments in medical education have developed from single pictures and simple animations to 3D model simulation experiments. At ...

• A novel connectivity kernel was proposed for elongated structures clustering.
• ‘Bridge’ shaped outliers and noise can be eliminated by the proposed kernel.
• Connectivity were considered during the calculation of similarity.
• The ...

Similarity calculation is one of the most critical steps of clustering analysis, especially for arbitrarily formed elongated structures. When it comes to Density Peak Clustering (DPC), using Euclidean distance solely to calculate the similarity ...

• ADESSA detects attacks in CPS when traffic is rare labeled, unbalanced and unknown attacks exist.
• ADESSA builds a balanced training set including high-information and low-information samples with limited budget.
• Adding low-...

As an industrial infrastructure, the safety and reliability of the Cyber-Physical System requires the effective anomaly detection. However, the existing detection methods have bottleneck in the face of insufficient training datasets. This work ...

Reputation evaluation is one of vital elements in contemporary social network-based applications. A common type of evaluation method is relying on the third party or a community-based rating. However, this type of method is encountering an issue caused ...

In 2014, a novel clustering algorithm called Density Peak Clustering (DPC) was proposed in journal Science, which has received great attention in many fields due to its simplicity and effectiveness. However, empirical studies have ...

• A FPC-based dynamic multi swarm PSO with enhanced learning strategy is presented.

Particle Swarm Optimization (PSO) is a well-known swarm intelligence algorithm and its performance primarily depends on the tradeoff between exploration and exploitation. In order to well balance the exploration and exploitation, this ...

Imbalanced datasets classification remains an important domain in machine learning. Conventional supervised learning algorithms tend to be biased towards the majority class when addressing imbalanced datasets, thus providing poor ...

• The method applied a novel manifold distance to calculate local densities of DPC.
• The used distance possesses global and local consistency adjustable characteristics.
• The improved DPC is suitable for both manifold and Gaussian-like ...

A novel density-based clustering algorithm, called Density Peak Clustering (DPC), has recently received great attention due to its efficiency in clustering performance and simplicity in implementation. However, empirical studies have demonstrated ...

• ADPCHFO applies modified density peaks clustering to cluster the minority instances.

Learning from imbalanced datasets poses a major challenge in data mining community. When dealing with imbalanced datasets, conventional classification algorithms generally perform poorly as they are originally designed to work under ...

Particle Swarm Optimization (PSO) algorithm has lately received great attention due to its powerful search capacity and simplicity in implementation. However, previous studies have demonstrated that PSO still suffers from two key ...

• The method applies multi-scale Gaussian mutations as basic mutation strategy.
• ...

The learning problem from imbalanced data sets poses a major challenge in data mining community. Although conventional support vector machine can generally show relatively robust performance in dealing with the classification problems ...

• ACFSVM focuses on the majority class with higher affinities and class probabilities.

With the rapid development of the Internet, online recruitment has gradually become mainstream. However, job seekers need to spend a lot of time to find a suitable job when there are a large variety of job information, which will seriously affect ...

• As an over-sampling method, RNSO does not require minority class instance available.

The learning problem from imbalanced data set poses a major challenge in data mining community. Conventional machine learning algorithms show poor performance in dealing with the classification problems of imbalanced data set since ...

In this paper, a novel information hiding method is proposed to embed data in the Word documents that use OOXML format. The 2007 version and more recent versions of MS Word are all based on the OOXML format. The main document body of OOXML document ...

A robust text watermarking approach has been proposed in this paper, in which the watermarks are embedded into the text by the transformation of characters’ RGB-style color in the OOXML format document. To get high watermark embedding capacity, two ...

• The proposed approach applies self-adaptive cost-sensitive SVM as basic weak leaner.
• The method modifies standard AdaBoost scheme to cost-sensitive one suitable for SVM.
• The method ensures the consistency of optimization objectives ...

Imbalanced data classification poses a major challenge in data mining community. Although standard support vector machine can generally show relatively robust performance in dealing with the classification problems of imbalanced data set, it is a ...

In this paper we provide the explicit expression and sharper bounds of the chordal metric between generalized singular values of Grassmann matrix pairs. The new results involve the constrained optimization problem of the form $\max_{U\in\mathbb{U}_n} f(U)$, ...

The purpose of this paper is to present an extended integrated layout and topology optimization method dealing with the multi-frame and multi-component fuselage structure systems design. Considering an aircraft or aerospace fuselage system including ...