Intelligent Data Analysis: An International Journal

Label noise detection has been widely studied in Machine Learning because of its importance in improving training data quality. Satisfactory noise detection has been achieved by adopting ensembles of classifiers. In this approach, an instance is assigned as mislabeled if a high proportion of members in the pool misclassifies it. Previous authors have empirically evaluated this approach; nevertheless, they mostly assumed that label noise is generated completely at random in a dataset. This is a strong assumption since other types of label noise are feasible in practice and can influence noise detection results. This work investigates the performance of ensemble noise detection under two different noise models: the Noisy at Random (NAR), in which the probability of label noise depends on the instance class, in comparison to the Noisy Completely at Random model, in which the probability of label noise is entirely independent. In this setting, we investigate the effect of class distribution on noise detection performance since it changes the total noise level observed in a dataset under the NAR assumption. Further, an evaluation of the ensemble vote threshold is conducted to contrast with the most common approaches in the literature. In many performed experiments, choosing a noise generation model over another can lead to different results when considering aspects such as class imbalance and noise level ratio among different classes.

The independence assumptions help Bayesian network classifier (BNC), e.g., Naive Bayes (NB), reduce structure complexity and perform surprisingly well in many real-world applications. Semi-naive Bayesian techniques seek to improve the classification performance by relaxing the attribute independence assumption. However, the study of dependence rather than independence has received more attention during the past decade and the validity of independence assumptions needs to be further explored. In this paper, a novel learning technique, called Adaptive Independence Thresholding (AIT), is proposed to automatically identify the informational independence and probabilistic independence. AIT can respectively tune the network topologies of BNC learned from training data and testing instance under the framework of target learning. Zero-one loss, bias, variance and conditional log likelihood are introduced to compare the classification performance in the experimental study. The extensive experimental results on a collection of 36 benchmark datasets from the UCI machine learning repository show that AIT is more effective than other learning techniques (such as structure extension, attribute weighting) and helps make the final BNCs achieve remarkable classification improvements.

In building a graphical model, accuracy in edge detection for the model structure is crucial for the quality of the model. We explored methods for improvement of false discovery rate(FDR) by devising an estimation procedure which is more data sensitive under some condition. The estimation is made by applying an EM method where the parameters include the density function under the null hypothesis (no edge) and the location parameters of the density functions under the alternative hypothesis (presence of edge). Our method is compared favorably with a most popular FDR tool in numerical experiments. We applied our method for analysing gene data of 800 genes and built a network of vector autoregressive model for the data.

Outlier detection has been widely explored and applied to different real-world problems. However, outlier characterization that consists in finding and understanding the outlying aspects of the anomalous observations is still challenging. In this paper, we present a new approach to simultaneously detect subspace outliers and characterize them. We introduce the Dimension-wise Local Outlier Factor (DLOF) function to quantify the degree of outlierness of the data points in each feature dimension. The obtained DLOFs are used in an outlier ensemble so as to detect and rank the anomalous points. Subsequently, the same DLOFs are analyzed in order to characterize the detected outliers with their relevant subspace and their same-type anomalies. Experiments on various datasets show the efficacy of our method. Indeed, we demonstrate through an experimental evaluation that the proposed approach is competitive compared to the existing solutions in terms of both detection and characterization accuracy.

As a research hotspot in ensemble learning, clustering ensemble obtains robust and highly accurate algorithms by integrating multiple basic clustering algorithms. Most of the existing clustering ensemble algorithms take the linear clustering algorithms as the base clusterings. As a typical unsupervised learning technique, clustering algorithms have difficulties properly defining the accuracy of the findings, making it difficult to significantly enhance the performance of the final algorithm. AGglomerative NESting method is used to build base clusters in this article, and an integration strategy for integrating multiple AGglomerative NESting clusterings is proposed. The algorithm has three main steps: evaluating the credibility of labels, producing multiple base clusters, and constructing the relation among clusters. The proposed algorithm builds on the original advantages of AGglomerative NESting and further compensates for the inability to identify arbitrarily shaped clusters. It can establish the proposed algorithm’s superiority in terms of clustering performance by comparing the proposed algorithm’s clustering performance to that of existing clustering algorithms on different datasets.

Exploiting label correlation is crucially important in multi-label learning, where each instance is associated with multiple labels simultaneously. Multi-label learning is more complex than single-label learning for that the labels tend to be correlated. Traditional multi-label learning algorithms learn independent classifiers for each label and employ ranking or threshold on the classification results. Most existing methods take label correlation as prior knowledge, which have worked well, but they failed to make full use of label dependency. As a result, the real relationship among labels may not be correctly characterized and the final prediction is not explicitly correlated. To address these problems, we propose a novel high-order multi-label learning algorithm of Label collAboration based Multi-laBel learning (LAMB). With regard to each label, LAMB utilizes collaboration between its own prediction and the prediction of other labels. Extensive experiments on various datasets demonstrate that our proposed LAMB algorithm achieves superior performance over existing state-of-the-art algorithms. In addition, one real-world dataset of channelrhodopsins chimeras is assessed, which would be of great value as pre-screen for membrane proteins function.

Data classification is a data mining task that consists of an algorithm adjusted by a training dataset that is used to predict an object’s class (unclassified) on analysis. A significant part of the performance of the classification algorithm depends on the dataset’s complexity and quality. Data Complexity involves the investigation of the effects of dimensionality, the overlap of descriptive attributes, and the classes’ separability. Data Quality focuses on the aspects such as noise data (outlier) and missing values. The factors Data Complexity and Data Quality are fundamental for the performance of classification. However, the literature has very few studies on the relationship between these factors and to highlight their significance. This paper applies Structural Equation Modeling and the Partial Least Squares Structural Equation Modeling (PLS-SEM) algorithm and, in an innovative manner, associates Data Complexity and Data Quality contributions to Classification Quality. Experimental analysis with 178 datasets obtained from the OpenML repository showed that the control of complexity improves the classification results more than data quality does. Additionally paper also presents a visual tool of datasets analysis about the classification performance perspective in the dimensions proposed to represent the structural model.

Bayesian network (BN) is one of the most powerful probabilistic models in the field of uncertain knowledge representation and reasoning. During the past decade, numerous approaches have been proposed to build directed acyclic graph (DAG) as the structural specification of BN. However, for most Bayesian network classifiers (BNCs) the directed edges in DAG substantially represent assertions of conditional independence rather than causal relationships although the learned joint probability distributions may fit data well, thus they cannot be applied to causal reasoning. In this paper, conditional entropy is introduced to measure causal uncertainty due to its asymmetry characteristic, and heuristic search strategy is applied to build Bayesian causal tree (BCT) by identifying significant causalities. The resulting highly scalable topology can represent causal relationship in terms of causal science, and corresponding joint probability can fit training data in terms of data science. Then ensemble learning strategy is applied to build Bayesian causal forest (BCF) with a set of BCTs, each taking different attribute as the root node to represent root cause for causality analysis. Extensive experiments performed on 32 public datasets from the UCI machine learning repository show that BCF achieves outstanding classification performance compared to state-of-the-art single-model BNCs (e.g., CFWNB), ensemble BNCs (e.g., WATAN, IWAODE, WAODE-MI and TAODE) and non-Bayesian learners (e.g., SVM, k-NN, LR).

Class imbalance of medical records is a critical challenge for disease classification in intelligent diagnosis. Existing machine learning algorithms usually assign equal weights to all classes, which may reduce classification accuracy of imbalanced records. In this paper, a new Imbalance Lessened Boosting (IMLBoost) algorithm is proposed to better classify imbalanced medical records, highlighting the contribution of samples in minor classes as well as hard and boundary samples. A tailored Cost-Fitting Loss (CFL) function is proposed to assign befitting costs to these critical samples. The first and second derivations of the CFL are then derived and embedded into the classical XGBoost framework. In addition, some feature analysis skills are utilized to further improve performance of the IMLBoost, which also can speed up the model training. Experimental results on five UCI imbalanced medical datasets have demonstrated the effectiveness of the proposed algorithm. Compared with other existing classification methods, IMLBoost has improved the classification performance in terms of F1-score, G-mean and AUC.

Influence Maximization (IM), an NP-hard central issue for social network research, aims to recognize the influential nodes in a network so that the message can spread faster and more effectively. A large number of existing studies mainly focus on the heuristic methods, which generally lead to sub-optimal solutions and suffer time-consuming and inapplicability for large-scale networks. Furthermore, the present community-aware random walk to analyze IM using network representation learning considers only the node’s influence or network community structures. No research has been found that surveyed both of them. Hence, the present study is designed to solve the IM problem by introducing a novel influence network embedding (NINE) approach and a novel influence maximization algorithm, namely NineIM, based on network representation learning. First, a mechanism that can capture the diffusion behavior proximity between network nodes is constructed. Second, we consider a more realistic social behavior assumption. The probability of information dissemination between network nodes (users) is different from other random walk based network representation learning. Third, the node influence is used to define the rules of random walk and then get the embedding representation of a social network. Experiments on four real-world networks indicate that our proposed NINE method outperforms four state-of-the-art network embedding baselines. Finally, the superiority of the proposed NineIM algorithm is reported by comparing four traditional IM algorithms. The code is available at https://github.com/baiyazi/NineIM.

In this work, we evaluate the impact of changing the semantic text representation on the performance of the AR-SVS (extended association rules in semantic vector spaces) algorithm on the sentiment polarity classification task on a paper reviews dataset. To do this, we use natural language processing techniques in conjunction with machine learning classifiers. In particular, we report the classification performance using the F 1 and accuracy metrics. The semantic representations that we used in our evaluation were chosen based on a systematic literature review, leading to an evaluation of AR-SVS with FastText, GloVe, and LDA2vec representations, with word2vec providing the baseline performance. The results of the experiments indicate that the choice of semantic text representation does not have major effects on the performance of AR-SVS for polarity classification. Furthermore, the results resemble those obtained in the original AR-SVS article, both in quantitative and qualitative terms. Thus, while direct improvements in classification performance were not found, we discuss other aspects and advantages of using different semantic representations.

In cybersecurity, intrusion detection systems (IDSes) are of vital importance, allowing different companies and their departments to identify malicious attacks from magnanimous network traffic; however, the effectiveness and stability of these artificial intelligence-based systems are challenged when coping with adversarial attacks. This work explores a creative framework based on a generative adversarial network (GAN) with a series of training algorithms that aims to generate instances of adversarial attacks and utilize them to help establish a new IDS based on a neural network that can replace the old IDS without knowledge of any of its parameters. Furthermore, to verify the quality of the generated attacks, a transfer mechanism is proposed for calculating the Frechet inception distance (FID). Experiments show that based on the original CICIDS2017 dataset, the proposed framework can generate four types of adversarial attacks (DDoS, DoS, Bruteforce, and Infiltration), which precipitate four types of classifiers (Decision Tree, Random Forest, Adaboost, and Deep Neural Network), set as black-box old IDSes, with low detection rates; additionally, the IDSes that the proposed framework newly establish have an average detection rate of 98% in coping with both generated adversarial and original attacks.

The dense deployment of small cell networks is a key feature of next generation mobile networks aimed at providing the necessary capacity increase. In order to reach an acceptable performance in such ultra-dense networks, real-time resource management is of great importance. Therefore, self-optimization networking is proposed as the only viable solution to increase the networks’ utility. This paper proposed a self-optimizing model to enhance network performance and guarantee the users’ QoS requirements by considering limited resources and using effective user association, carrier scheduling and handover optimization algorithms. In order to maximize the network performance, we applied the smart backhauling technique in order to analyze the signaling to increase the validity of the decision making process. Based on the semantic information extracted from the access layer, the network decision-making center is able to adjust the network parameters and resource allocation effectively. The goal function is defined as maximizing the total energy efficiency by considering the transmission power, energy harvesting capability and the user QoS constraints so that the idle small cells are considered turned off temporarily to boost the power efficiency. Although the optimization problem is non-convex, a quadratic mixed-integer function is solved to obtain a global optimal solution. Since the actual implementation of the real-time algorithm has high computational complexity, two algorithms with different complexity levels are proposed. These algorithms use the carrier matching feature and optimal transmission power for problem-solving. The simulation results prove that, despite the increased computational complexity, effective resource allocation and optimal HO relations made the proposed approach capable to increase performance indices such as network throughput by up to 30%.

The concept of intuitionistic fuzzy set has been found to be highly useful to handle vagueness in data. Based on intuitionistic fuzzy set theory, intuitionistic fuzzy clustering algorithms are proposed and play an important role in image segmentation. However, due to the influence of initialization and the presence of noise in the image, intuitionistic fuzzy clustering algorithm cannot acquire the satisfying performance when applied to segment images corrupted by noise. In order to solve above problems, a robust intuitionistic fuzzy clustering with bias field estimation (RIFCB) is proposed for noisy image segmentation in this paper. Firstly, a noise robust intuitionistic fuzzy set is constructed to represent the image by using the neighboring information of pixels. Then, initial cluster centers in RIFCB are adaptively determined by utilizing the frequency statistics of gray level in the image. In addition, in order to offset the information loss of the image when constructing the intuitionistic fuzzy set of the image, a new objective function incorporating a bias field is designed in RIFCB. Based on the new initialization strategy, the intuitionistic fuzzy set representation, and the incorporation of bias field, the proposed method preserves the image details and is insensitive to noise. Experimental results on some Berkeley images show that the proposed method achieves satisfactory segmentation results on images corrupted by different kinds of noise in contrast to conventional fuzzy clustering algorithms.