As a state-of-the-art multi-class supervised novelty detection method, supervised novelty detection-support vector machine (SND-SVM) is extended from one-class support vector machine (OC-SVM). It still requires to slove a more time-consuming quadratic programming (QP) whose scale is the number of training samples multiplied by the number of normal classes. In order to speed up SND-SVM learning, we

Dimensionality reduction (DR) has been widely used to deal with high-dimensional data, and plays an important role in alleviating the so-called “curse of dimensionality”. In this paper, we propose a novel unsupervised DR method with applications to face recognition, i.e., Nonnegative Representation based Discriminant Projection (NRDP). Different with other locality or globality preserving DR methods

One of the applications of center-based clustering algorithms such as K-means is partitioning data points into K clusters. In some examples, the feature space relates to the underlying problem we are trying to solve, and sometimes we can obtain a suitable feature space. Nevertheless, while K-means is one of the most efficient offline clustering algorithms, it is not equipped to estimate the number

Clustering is a fundamental approach to discover the valuable information in data mining and machine learning. Density peaks clustering is a typical density based clustering and has received increasing attention in recent years. However DPC and most of its improvements still suffer from some drawbacks. For example, it is difficult to find peaks in the sparse cluster regions; assignment for the remaining

Among machine learning paradigms, unsupervised transductive transfer learning is useful when no labeled data from the target domain are available at training time, but there is accessible unlabeled target data during training phase instead. The current paper proposes a novel unsupervised transductive transfer learning method to find the specific and shared features across the source and the target

With the introduction of the interval-valued intuitionistic fuzzy sets, the interval-valued intuitionistic fuzzy numbers are used instead of precise numbers to provide fuzzy characterization of feature attribute values and misclassification loss function values, which is more in line with the realistic fuzzy decision-making environment. Also, the constructive covering algorithm is introduced into the

This research mainly focuses on presenting an innovative study of a multi-stage multi-objective fixed-charge solid transportation problem (MMFSTP) with a green supply chain network system under an intuitionistic fuzzy environment. The most controversial issue in recent years is that greenhouse gas emissions such as carbon dioxide, methane, etc. induce air pollution and global warming, thus motivating

In this paper, a new adaptive super-twisting sliding mode control (STSMC) scheme based on a double loop fuzzy neural network (DLFNN) is proposed to solve the problem of the external disturbances and approximate the unknown model for a micro gyroscopes. The STSMC algorithm can effectively suppress chattering since it can hide the high-frequency switching part in the high-order derivative of the sliding

Cushing's disease (CD) is a rare disease that occurs in 1.2–1.4 persons per million population per year. Recurrence prediction after transsphenoidal surgery (TSS) is important for determining individual treatment and follow-up strategies. Between 2000 and 2017, 354 CD patients with initial postoperative remission and long-term follow-up data were enrolled from Peking union medical college hospital

This study introduces the evolutionary multi-objective version of seagull optimization algorithm (SOA), entitled Evolutionary Multi-objective Seagull Optimization Algorithm (EMoSOA). In this algorithm, a dynamic archive concept, grid mechanism, leader selection, and genetic operators are employed with the capability to cache the solutions from the non-dominated Pareto. The roulette-wheel method is

Recently, deep neural networks have demonstrated their efficiency in image classification tasks, which are commonly achieved by an extended depth and width of network architecture. However, poor convergence, over-fitting and gradient disappearance might be generated with such comprehensive architectures. Therefore, DenseNet is developed to address these problems. Although DenseNet adopts bottleneck

With the increasing complexity of scientific research and the expanding scale of projects, scientific research cooperation is an important trend in large-scale research. The analysis of co-authorship networks is a big data problem due to the expanding scale of the literature. Without sufficient data mining, research cooperation will be limited to a similar group, namely, a “small group”, in the co-author

Review rating prediction is commonly approached from the perspective of either Collaborative Filtering (CF) or Sentiment Classification (SC). CF-based approach usually resorts to matrix factorization based on user–item interaction, and does not fully utilize the valuable review text features. In contrast, SC-based approach is focused on mining review content, but can just incorporate some user- and

In the field of machine learning, offline training and online training occupy the same important position because they coexist in many real applications. The extreme learning machine (ELM) has the characteristics of fast learning speed and high accuracy for offline training, and online sequential ELM (OS-ELM) is a variant of ELM that supports online training. With the explosive growth of data volume

Fault detection and isolation (FDI) is considered as one of the most critical problems in biological processes. Therefore, in this paper, we consider a new FDI framework that aims to improve the monitoring of biological processes. To do that, a machine learning-based statistical hypothesis approach, which can identify the model, detect and isolate the faults, will be developed. In the developed approach

Clinical quantitative information contains crucial measurable expressions of patients’ diseases and treatment conditions, which are commonly exist in free-text electronic medical records. Although the clinical quantitative information is of considerable significance in assisting the analysis of health care, few researches have yet focused on the topic and it remains an ongoing challenge. Focusing on

5G cellular networks are expected to be the key infrastructure to deliver the emerging services. These services bring new requirements and challenges that obstruct the desired goal of forthcoming networks. Mobile operators are rethinking their network design to provide more flexible, dynamic, cost-effective and intelligent solutions. This paper starts with describing the background of the 5G wireless

The rapid development and application of AI in intelligent transportation systems has widely impacted daily life. The application of an intelligent visual aid for traffic sign information recognition can provide assistance and even control vehicles to ensure safe driving. The field of autonomous driving is booming, and great progress has been made. Many traffic sign recognition algorithms based on

Time series data is ubiquitous in financial, biomedical, and other areas. Anomaly detection in time series has been widely researched in these areas. However, most existing algorithms suffer from “curse of dimension” and may lose some information in the process of feature extraction. In this paper, we propose two new data structures named interval table (ITable) and extend interval table (EITable)

There exist two formulations of the theory of rough sets, consisting of the conceptual formulations and the computational formulations. Class-specific and classification-based attribute reducts are two crucial notions in three-way probabilistic rough set models. In terms of conceptual formulations, the two types of attribute reducts can be defined by considering probabilistic positive or negative region

The particle swarm optimization (PSO) algorithm is a popular evolutionary computation approach that has received an ever-increasing interest in the past decade owing to its wide application potential. Despite the many variants of the PSO algorithm with improved search ability by means of both the convergence rate and the population diversity, the local optima problem remains a major obstacle that hinders

Since p-spectral clustering has good performance in many practical problems, it has attracted great attention. The Cheeger cut criterion is used in p-spectral clustering to do graph partition. However, due to the improper affinity measure and outliers, the original p-spectral clustering algorithm is not effective in dealing with manifold data. To solve this problem, we propose a manifold p-spectral

The medical literature is the most important way to demonstrate academic achievements and academic exchanges. Massive medical literature has become a huge treasure trove of knowledge. It is necessary to automatically extract implicit medical knowledge from the medical literature. Medical relation extraction aims to automatically extract medical relations from the medical text for various medical researches

A reliable adaptive hybrid classifier (hAHC), which combines a posture-based adaptive signal segmentation algorithm with a multi-layer perceptron (MLP) classifier, together with a plurality voting approach, was proposed and evaluated in this study. The hAHC model was evaluated using a real-time posture recognition framework that sought to identify five behaviours (sitting, walking, standing, running

Many real-world datasets nowadays are of regression type, while only a few dimensionality reduction methods have been developed for regression problems. On the other hand, most existing regression methods are based on the computation of the covariance matrix, rendering them inefficient in the reduction process. Therefore, a BMA-based multi-objective feature selection method, GBMA, is introduced by

Optimal scale combination selection plays a critical role for knowledge discovery in multi-scale decision tables (MDTs) and has attracted considerable attention. However, searching for all optimal scale combinations from the scale collection may result in a combinatorial explosion, and the existing approaches are time-consuming. The main goal of this study is to improve the efficiency of searching

Learning low-dimensional vector representations of words from a large corpus is one of the basic tasks in natural language processing (NLP). The existing universal word embedding model learns word vectors mainly through grammar and semantic information from the context, while ignoring the sentiment information contained in the words. Some approaches, although they model sentiment information in the

Many real-world data have multiple class labels known as multi-label data, where the labels are correlated with each other, and as such, they are not independent. Since these data are usually high-dimensional, and the current multi-label feature selection methods have not been precise enough, then a new feature selection method is necessary. In this paper, for the first time, we have modeled the problem

The problem of frequent itemset mining is one of the more important problems in data mining which has been extensively employed across a wide range of other relevant tasks such as market basket analysis in marketing, or text analysis in text mining applications. The majority of the deterministic frequent itemset mining algorithms which have been proposed in recent years use some sort or another of

Outlier ensembles that use more base detectors recently become an attractive approach to solving problems of single detectors. However, existing outlier ensembles often assume that base detectors make independent errors, which is difficult to satisfy in practical applications. To this end, this paper proposes a dynamic outlier ensemble to loose this error independence assumption. In our method, it

Disaster has great destructiveness and a wide influence on urban and rural construction, and more than one place is affected by disaster. Thus, it needs to take multiple disaster points into account. In view of different rescue missions, both medical rescue teams and search rescue teams also need to be dispatched. That is the multi-sided matching among medical rescue teams, search rescue teams and

The product knapsack problem (PKP) is a new variation of the knapsack problem which arises in social choice computation. Although some deterministic algorithms have been reported to handle small-scale problems, the solution to the middle and large-scale problems is still lack of progress. For efficiently solving this problem, a new ideal of solving PKP by evolutionary algorithms is proposed in the

Side-information based exponential discriminant analysis (SIEDA) is more efficient than side-information based linear discriminant analysis (SILDA) in computing the discriminant vectors because it maximizes the Fisher criterion function. In this paper, we develop a novel criterion, named side-information based weighted exponential discriminant analysis (SIWEDA), that is based on the classical SIEDA

In this paper, our focus is on using a rule-based approach to develop agents with artificial general intelligence. In rule-based systems, developing effective rules is a huge challenge, and coding rules for agents requires a large amount of manual work. Association rules mining (ARM) can be used for discovering specific rules from data sets and determining relationships between data sets. In this paper

Task-space control needs the inverse kinematics solution or Jacobian matrix for the transformation from task space to joint space. However, they are not always available for redundant robots because there are more joint degrees-of-freedom than Cartesian degrees-of-freedom. Intelligent learning methods, such as neural networks (NN) and reinforcement learning (RL) can learn the inverse kinematics solution

Optimization is used in different fields of engineering to solve complex problems. In image processing, multilevel thresholding requires to find the optimal configuration of thresholds to obtain accurate segmented images. In this case, the use of two-dimensional histograms is helpful because they permit us to combine information from the image preserving different features. This paper introduces a

The rapid development in 5G cellular and IoT technologies is expected to be deployed widespread in the next few years. At the same time, crime rates are also increasing to a greater extent while the investigation officers are held responsible to deal with a broad range of cyber and internet issues in investigations. Therefore, advanced IT technologies and IoT devices can be deployed to ease the investigation

Image has become a main medium of Internet information dissemination, makes it easy for an Internet visitor to get pornographic images with just few clicks on websites. It is necessary to build pornographic image recognition systems since uncontrolled spreading of adult content could be harm to the adolescents. Previous solutions for pornographic image recognition are usually based on hand-crafted

Predicting the depth of a scene from monocular infrared images, which plays a crucial role in understanding three-dimensional structures, is one of the challenging tasks in machine learning and computer vision. Considering the lack of texture and color information in infrared images, a novel discrete convolutional conditional random field network is proposed for depth estimation. The proposed method

A causative attack which manipulates training samples to mislead learning is a common attack scenario. Current countermeasures reduce the influence of the attack to a classifier with the loss of generalization ability. Therefore, the collected samples should be analyzed carefully. Most countermeasures of current causative attack focus on data sanitization and robust classifier design. To our best knowledge

Attribute reduction in formal decision contexts has become one of the key issues in the research and development of formal concept analysis (FCA) and its applications. As far as we know, however, most of the existing reduction methods for formal decision contexts are time-consuming especially for the large-scale data. This paper investigates the attribute reduction method for large-scale formal decision

As an emerging and applicable method, deep learning (DL) has attracted much attention in recent years. With the development of DL and the massive of publications and researches in this direction, a comprehensive analysis of DL is necessary. In this paper, from the perspective of bibliometrics, a comprehensive analysis of publications of DL is deployed from 2007 to 2019 (the first publication with keywords

A heuristic Dual sampling domain Reduction-based Optimal Rapidly-exploring Random Tree scheme is proposed by guiding the planning procedure of the optimal rapidly-exploring random tree (RRT*) method through learning environmental knowledge. The scheme aims to plan low fuel expenditure, easy-execution, and low collision probability paths online for an unmanned surface vehicle (USV) under constraints

In this paper, we propose a new 3D high mesh deformation technique to extract intuitive and interpretable deformation and alignment components. Our framework is based on a fast Beta wavelet transform for a multi-resolution analysis relying on multi-library wavelet neural network architecture. The main drawback of 3D high mesh deformation is the large number of triangles necessary to characterize a

There are few studies in the literature to address the multi-objective multi-label feature selection for the classification of video data using evolutionary algorithms. Selecting the most appropriate subset of features is a significant problem while maintaining/improving the accuracy of the prediction results. This study proposes a framework of parallel multi-objective Non-dominated Sorting Genetic

The problem of food segmentation is quite challenging since food is characterized by intrinsic high intra-class variability. Also, segmentation of food images taken in-the-wild may be characterized by acquisition artifacts, and that could be problematic for the segmentation algorithms. A proper evaluating of segmentation algorithms is of paramount importance for the design and improvement of food analysis

Design by contract is a software development methodology that uses contracts for defining interfaces among interacting components of a software system. Graph transformation system is used to specify the behavioral aspects of software components by defining the pre- and post-conditions of methods as contracts. In this paper, we focus on testing executable software models specified by a graph transformation

In the world of big data, large amounts of images are available in social media, corporate and even personal collections. A collection may grow quickly as new images are generated at high rates. The new images may cause changes in the distribution of existing classes or the emergence of new classes, resulting in the collection being dynamic and having concept drift. For efficient image retrieval from

Many kinds of real-world multi-way signal, like color images, videos, etc., are represented in tensor form and may often be corrupted by outliers. To recover an unknown signal tensor corrupted by outliers, tensor robust principal component analysis (TRPCA) serves as a robust tensorial modification of the fundamental PCA. Recently, a successful TRPCA model based on the tubal nuclear norm (TNN) (Lu et

The fuzzy c-means clustering with guided image filter (GF) is a useful method for image segmentation. The single-channel GF can be efficiently applied to the gray-scale guidance image, but for the color guidance image, due to the high run-time overhead on the calculation of the inverse of the covariance matrix, it is a hard work to perform the multi-channel GF. To address this issue, we propose a novel

Electronic health records (EHRs) have been widely used to help physicians to make decisions by predicting medical events such as diseases, prescriptions, outcomes, and so on. How to represent patient longitudinal medical data is the key to making these predictions. Recurrent neural network (RNN) is a popular model for patient longitudinal medical data representation from the view of patient status

Interval type-2 trapezoidal fuzzy sets, as a particular form of interval type-2 fuzzy sets, can precisely characterize the subjective assessments and qualitative evaluations of a group of experts. In this paper, a novel likelihood-based interval type-2 trapezoidal fuzzy multi-expert multi-criteria decision-making approach is proposed. To do so, the concepts of likelihood-based performance index, likelihood-based

The problem of energy efficiency in cognitive radio sensor networks (CRSN) is mainly caused by the limited energy of sensor nodes and other channel-related operations for data transmission. The unequal clustering method should be considered for balancing the energy consumption among the cluster heads (CHs) for prolonging the network lifetime. The CH selection should consider the number of accessible

Local binary feature learning has attracted a lot of researches in image recognition due to its vital effectiveness. Generally, in the traditional local feature learning methods, a projection is learned to map the patches of image into binary features and then a codebook is generated by clustering the binary features with K-means clustering. However, these local feature learning methods, such as compact

Considering contextual features is a key issue in sentiment analysis. Existing approaches including convolutional neural networks (CNNs) and recurrent neural networks (RNNs) lack the ability to account and prioritize informative contextual features that are necessary for better sentiment interpretation. CNNs present limited capability since they are required to be very deep, which can lead to the gradient

The use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features

In this paper, we have devised a multiobjective optimization solution framework for solving the problem of gene expression data clustering in reduced feature space. Here clustering problem is viewed from two different aspects: clustering of genes in reduced sample space or clustering of samples in reduced gene space. Three objective functions: two internal cluster validity indices and the count on

In this paper, the nonlinear multi-agent system which contains uncertainty and is controlled by state-constraint impulsive protocol is taken into consideration. For the uncertainty of the multi-agent system, it is replaced by fuzzy logic system approximately and a judgement strategy which only contains the relative information with neighbors is proposed in this paper. In order to do research in state-constraint

Fuzzy kernels are a special kind of kernels which are usually employed to calculate the upper and lower approximations, as well as the positive region in kernelized fuzzy rough sets, and the positive region characterizes the degree of consistency between conditional attributes and decision attributes. When the classification hyperplane exists between two classes of samples, the positive region is transformed

Despite the benefits mentioned in previous works of automatically acquiring skills for using them in hierarchical reinforcement learning algorithms such as solving the curse of dimensionality, improving exploration, and speeding up value propagation, they have not paid much attention to evaluating the effect of each skill on these factors. In this paper, we show that depending on the given task, a