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Advances in Data Mining, Machine Learning and Causal Inference and Their Applications

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 15218

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Special Issue Editors

Dr. Debo Cheng

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Guest Editor

STEM, University of South Australia, Adelaide 5001, Australia
Interests: data mining; machine learning; causal inference; artificial neural networks and artificial intelligence

Dr. Junbo Ma

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Guest Editor

Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Interests: image/video processing; feature selection; sparse learning; multimodal data analysis; graph neural networks; computational neuroscience, and medical image processing

Dr. Rongyao Hu

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Guest Editor

CBICA, University of Pennsylvania, Philadelphia, PA 19104, USA
Interests: machine learning; pattern recognition; medical image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, the cores of artificial intelligence, including data mining, machine learning and causal inference, have gained increasing attention across many areas, such as education, economics, health and computer version. A large number of works have promptly been developed in computer science and there have been corresponding applications in engineering, industry, economics, health, biology, and so on. Computational theoretics and methodologies play a critical role in the core of artificial intelligence.

This Special Issue focuses on the theoretical and methodological methods in data science, especially in the cores of artificial intelligence, with topics including but not limited to those in the following fields: computer version, natural language processing, bioinformatics, knowledge graph, knowledge engineering, mathematics, explainable artificial intelligence (XAI), distributed computation, multiagent technology, fuzzy systems, deep learning, causal discovery, causal inference, latent variables, selection bias, and graphical causal modelling.

Dr. Debo Cheng
Dr. Junbo Ma
Dr. Rongyao Hu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

feature extraction/selection or dimensionality reduction and their applications
knowledge graph, knowledge engineering
natural language processing
bioinformatics
retrieval methods
supervised/unsupervised/semi-supervised/transfer learning
computational social science such recommendation system, and persuasive computing
incremental learning (or online learning)
data fusion and multi-source multimedia data
explainable artificial intelligence (XAI)
causal discovery
causal inference, such as average causal effect estimation, Heterogeneous causal estimates
fairness, discrimination detection

Published Papers (13 papers)

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Research

16 pages, 6908 KiB

Open AccessArticle

Multiclass Sentiment Prediction of Airport Service Online Reviews Using Aspect-Based Sentimental Analysis and Machine Learning

by Mohammed Saad M. Alanazi, Jun Li and Karl W. Jenkins

Mathematics 2024, 12(5), 781; https://doi.org/10.3390/math12050781 - 6 Mar 2024

Viewed by 736

Abstract

Airport service quality ratings found on social media such as Airline Quality and Google Maps offer invaluable insights for airport management to improve their quality of services. However, there is currently a lack of research analysing these reviews by airport services using sentimental analysis approaches. This research applies multiclass models based on Aspect-Based Sentimental Analysis to conduct a comprehensive analysis of travellers’ reviews, in which the major airport services are tagged by positive, negative, and non-existent sentiments. Seven airport services commonly utilised in previous studies are also introduced. Subsequently, various Deep Learning architectures and Machine Learning classification algorithms are developed, tested, and compared using data collected from Twitter, Google Maps, and Airline Quality, encompassing travellers’ feedback on airport service quality. The results show that the traditional Machine Learning algorithms such as the Random Forest algorithm outperform Deep Learning models in the multiclass prediction of airport service quality using travellers’ feedback. The findings of this study offer concrete justifications for utilising multiclass Machine Learning models to understand the travellers’ sentiments and therefore identify airport services required for improvement. Full article

(This article belongs to the Special Issue Advances in Data Mining, Machine Learning and Causal Inference and Their Applications)

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23 pages, 3857 KiB

Open AccessArticle

Invariant Feature Learning Based on Causal Inference from Heterogeneous Environments

by Hang Su and Wei Wang

Mathematics 2024, 12(5), 696; https://doi.org/10.3390/math12050696 - 27 Feb 2024

Viewed by 1118

Abstract

Causality has become a powerful tool for addressing the out-of-distribution (OOD) generalization problem, with the idea of invariant causal features across domains of interest. Most existing methods for learning invariant features are based on optimization, which typically fails to converge to the optimal solution. Therefore, obtaining the variables that cause the target outcome through a causal inference method is a more direct and effective method. This paper presents a new approach for invariant feature learning based on causal inference (IFCI). IFCI detects causal variables unaffected by the environment through the causal inference method. IFCI focuses on partial causal relationships to work efficiently even in the face of high-dimensional data. Our proposed causal inference method can accurately infer causal effects even when the treatment variable has more complex values. Our method can be viewed as a pretreatment of data to filter out variables whose distributions change between different environments, and it can then be combined with any learning method for classification and regression. The result of empirical studies shows that IFCI can detect and filter out environmental variables affected by the environment. After filtering out environmental variables, even a model with a simple structure and common loss function can have strong OOD generalization capability. Furthermore, we provide evidence to show that classifiers utilizing IFCI achieve higher accuracy in classification compared to existing OOD generalization algorithms. Full article

(This article belongs to the Special Issue Advances in Data Mining, Machine Learning and Causal Inference and Their Applications)

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21 pages, 4933 KiB

Open AccessFeature PaperArticle

Enhancing Medical Decision Making: A Semantic Technology-Based Framework for Efficient Diagnosis Inference

by Dizza Beimel and Sivan Albagli-Kim

Mathematics 2024, 12(4), 502; https://doi.org/10.3390/math12040502 - 6 Feb 2024

Viewed by 688

Abstract

In the dynamic landscape of healthcare, decision support systems (DSS) confront continuous challenges, especially in the era of big data. Background: This study extends a Q&A-based medical DSS framework that utilizes semantic technologies for disease inference based on a patient’s symptoms. The framework inputs “evidential symptoms” (symptoms experienced by the patient) and outputs a ranked list of hypotheses, comprising an ordered pair of a disease and a characteristic symptom. Our focus is on advancing the framework by introducing ontology integration to semantically enrich its knowledgebase and refine its outcomes, offering three key advantages: Propagation, Hierarchy, and Range Expansion of symptoms. Additionally, we assessed the performance of the fully implemented framework in Python. During the evaluation, we inspected the framework’s ability to infer the patient’s disease from a subset of reported symptoms and evaluated its effectiveness in ranking it prominently among hypothesized diseases. Methods: We conducted the expansion using dedicated algorithms. For the evaluation process, we defined various metrics and applied them across our knowledge base, encompassing 410 patient records and 41 different diseases. Results: We presented the outcomes of the expansion on a toy problem, highlighting the three expansion advantages. Furthermore, the evaluation process yielded promising results: With a third of patient symptoms as evidence, the framework successfully identified the disease in 94% of cases, achieving a top-ranking accuracy of 73%. Conclusions: These results underscore the robust capabilities of the framework, and the enrichment enhances the efficiency of medical experts, enabling them to provide more precise and informed diagnostics. Full article

(This article belongs to the Special Issue Advances in Data Mining, Machine Learning and Causal Inference and Their Applications)

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25 pages, 2824 KiB

Open AccessArticle

Knowledge Granularity Attribute Reduction Algorithm for Incomplete Systems in a Clustering Context

by Baohua Liang, Erli Jin, Liangfen Wei and Rongyao Hu

Mathematics 2024, 12(2), 333; https://doi.org/10.3390/math12020333 - 19 Jan 2024

Viewed by 592

Abstract

The phenomenon of missing data can be seen everywhere in reality. Most typical attribute reduction models are only suitable for complete systems. But for incomplete systems, we cannot obtain the effective reduction rules. Even if there are a few reduction approaches, the classification accuracy of their reduction sets still needs to be improved. In order to overcome these shortcomings, this paper first defines the similarities of intra-cluster objects and inter-cluster objects based on the tolerance principle and the mechanism of knowledge granularity. Secondly, attributes are selected on the principle that the similarity of inter-cluster objects is small and the similarity of intra-cluster objects is large, and then the knowledge granularity attribute model is proposed under the background of clustering; then, the IKAR algorithm program is designed. Finally, a series of comparative experiments about reduction size, running time, and classification accuracy are conducted with twelve UCI datasets to evaluate the performance of IKAR algorithms; then, the stability of the Friedman test and Bonferroni–Dunn tests are conducted. The experimental results indicate that the proposed algorithms are efficient and feasible. Full article

(This article belongs to the Special Issue Advances in Data Mining, Machine Learning and Causal Inference and Their Applications)

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21 pages, 1137 KiB

Open AccessArticle

An Out-of-Distribution Generalization Framework Based on Variational Backdoor Adjustment

by Hang Su and Wei Wang

Mathematics 2024, 12(1), 85; https://doi.org/10.3390/math12010085 - 26 Dec 2023

Cited by 1 | Viewed by 566

Abstract

In practical applications, learning models that can perform well even when the data distribution is different from the training set are essential and meaningful. Such problems are often referred to as out-of-distribution (OOD) generalization problems. In this paper, we propose a method for OOD generalization based on causal inference. Unlike the prevalent OOD generalization methods, our approach does not require the environment labels associated with the data in the training set. We analyze the causes of distributional shifts in data from a causal modeling perspective and then propose a backdoor adjustment method based on variational inference. Finally, we constructed a unique network structure to simulate the variational inference process. The proposed variational backdoor adjustment (VBA) framework can be combined with any mainstream backbone network. In addition to theoretical derivation, we conduct experiments on different datasets to demonstrate that our method performs well in prediction accuracy and generalization gaps. Furthermore, by comparing the VBA framework with other mainstream OOD methods, we show that VBA performs better than mainstream methods. Full article

(This article belongs to the Special Issue Advances in Data Mining, Machine Learning and Causal Inference and Their Applications)

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17 pages, 7251 KiB

Open AccessArticle

Depth Map Super-Resolution Based on Semi-Couple Deformable Convolution Networks

by Botao Liu, Kai Chen, Sheng-Lung Peng and Ming Zhao

Mathematics 2023, 11(21), 4556; https://doi.org/10.3390/math11214556 - 5 Nov 2023

Cited by 1 | Viewed by 1047

Abstract

Depth images obtained from lightweight, real-time depth estimation models and consumer-oriented sensors typically have low-resolution issues. Traditional interpolation methods for depth image up-sampling result in a significant information loss, especially in edges with discontinuous depth variations (depth discontinuities). To address this issue, this paper proposes a semi-coupled deformable convolution network (SCD-Net) based on the idea of guided depth map super-resolution (GDSR). The method employs a semi-coupled feature extraction scheme to learn unique and similar features between RGB images and depth images. We utilize a Coordinate Attention (CA) to suppress redundant information in RGB features. Finally, a deformable convolutional module is employed to restore the original resolution of the depth image. The model is tested on NYUv2, Middlebury, Lu, and a Real-Sense real-world dataset created using an Intel Real-sense D455 structured-light camera. The super-resolution accuracy of SCD-Net at multiple scales is much higher than that of traditional methods and superior to recent state-of-the-art (SOTA) models, which demonstrates the effectiveness and flexibility of our model on GDSR tasks. In particular, our method further solves the problem of an RGB texture being over-transferred in GDSR tasks. Full article

(This article belongs to the Special Issue Advances in Data Mining, Machine Learning and Causal Inference and Their Applications)

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17 pages, 994 KiB

Open AccessArticle

One-Step Clustering with Adaptively Local Kernels and a Neighborhood Kernel

by Cuiling Chen, Zhijun Hu, Hongbin Xiao, Junbo Ma and Zhi Li

Mathematics 2023, 11(18), 3950; https://doi.org/10.3390/math11183950 - 17 Sep 2023

Viewed by 697

Abstract

Among the methods of multiple kernel clustering (MKC), some adopt a neighborhood kernel as the optimal kernel, and some use local base kernels to generate an optimal kernel. However, these two methods are not synthetically combined together to leverage their advantages, which affects the quality of the optimal kernel. Furthermore, most existing MKC methods require a two-step strategy to cluster, i.e., first learn an indicator matrix, then executive clustering. This does not guarantee the optimality of the final results. To overcome the above drawbacks, a one-step clustering with adaptively local kernels and a neighborhood kernel (OSC-ALK-ONK) is proposed in this paper, where the two methods are combined together to produce an optimal kernel. In particular, the neighborhood kernel improves the expression capability of the optimal kernel and enlarges its search range, and local base kernels avoid the redundancy of base kernels and promote their variety. Accordingly, the quality of the optimal kernel is enhanced. Further, a soft block diagonal (BD) regularizer is utilized to encourage the indicator matrix to be BD. It is helpful to obtain explicit clustering results directly and achieve one-step clustering, then overcome the disadvantage of the two-step strategy. In addition, extensive experiments on eight data sets and comparisons with six clustering methods show that OSC-ALK-ONK is effective. Full article

(This article belongs to the Special Issue Advances in Data Mining, Machine Learning and Causal Inference and Their Applications)

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13 pages, 1371 KiB

Open AccessArticle

A Communication-Efficient Federated Text Classification Method Based on Parameter Pruning

by Zheng Huo, Yilin Fan and Yaxin Huang

Mathematics 2023, 11(13), 2804; https://doi.org/10.3390/math11132804 - 21 Jun 2023

Viewed by 926

Abstract

Text classification is an important application of machine learning. This paper proposes a communication-efficient federated text classification method based on parameter pruning. In the federated learning architecture, the data distribution of different participants is not independent and identically distributed; a federated word embedding model FedW2V is proposed. Then the TextCNN model is extended to the federated architecture. To reduce the communication cost of the federated TextCNN model, a parameter pruning algorithm called FedInitPrune is proposed, which reduces the amount of communication data both in the uplink and downlink during the parameter transmission phase. The algorithms are tested on real-world datasets. The experimental results show that when the text classification model accuracy reduces by less than 2%, the amount of federated learning communication parameters can be reduced by 74.26%. Full article

(This article belongs to the Special Issue Advances in Data Mining, Machine Learning and Causal Inference and Their Applications)

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25 pages, 3689 KiB

Open AccessArticle

Authenticating q-Gram-Based Similarity Search Results for Outsourced String Databases

by Liangyong Yang, Haizhou Ye, Xuyang Liu, Yijun Mao and Jilian Zhang

Mathematics 2023, 11(9), 2128; https://doi.org/10.3390/math11092128 - 1 May 2023

Cited by 1 | Viewed by 1077

Abstract

Approximate string searches have been widely applied in many fields, such as bioinformatics, text retrieval, search engines, and location-based services (LBS). However, the approximate string search results from third-party servers may be incorrect due to the possibility of malicious third parties or compromised servers. In this paper, we design an authenticated index structure (AIS) for string databases, which is based on the Merkle hash tree (MHT) method and the q-gram inverted index. Our AIS can facilitate verification object (VO) construction for approximate string searches with edit distance thresholds. We design an efficient algorithm named GS² for VO construction at the server side and search result verification at the user side. We also introduce an optimization method called GS²-opt that can reduce VO size dramatically. Finally, we conduct extensive experiments on real datasets to show that our proposed methods are efficient and promising. Full article

(This article belongs to the Special Issue Advances in Data Mining, Machine Learning and Causal Inference and Their Applications)

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16 pages, 1798 KiB

Open AccessArticle

MFANet: A Collar Classification Network Based on Multi-Scale Features and an Attention Mechanism

by Xiao Qin, Shanshan Ya, Changan Yuan, Dingjia Chen, Long Long and Huixian Liao

Mathematics 2023, 11(5), 1164; https://doi.org/10.3390/math11051164 - 27 Feb 2023

Cited by 1 | Viewed by 1157

Abstract

The collar is an important part of a garment that reflects its style. The collar classification task is to recognize the collar type in the apparel image. In this paper, we design a novel convolutional module called MFA (multi-scale features attention) to address the problems of high noise, small recognition target and unsatisfactory classification effect in collar feature recognition, which first extracts multi-scale features from the input feature map and then encodes them into an attention weight vector to enhance the representation of important parts, thus improving the ability of the convolutional block to combat noise and extract small target object features. It also reduces the computational overhead of the MFA module by using the depth-separable convolution method. Experiments on the collar dataset Collar6 and the apparel dataset DeepFashion6 (a subset of the DeepFashion database) show that MFANet is able to perform at a relatively small number of collars. MFANet can achieve better classification performance than most current mainstream convolutional neural networks for complex collar images with less computational overhead. Experiments on the standard dataset CIFAR-10 show that MFANet also outperforms current mainstream image classification algorithms. Full article

(This article belongs to the Special Issue Advances in Data Mining, Machine Learning and Causal Inference and Their Applications)

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16 pages, 641 KiB

Open AccessArticle

Performance Evaluation of a Cloud Datacenter Using CPU Utilization Data

by Chen Li, Junjun Zheng, Hiroyuki Okamura and Tadashi Dohi

Mathematics 2023, 11(3), 513; https://doi.org/10.3390/math11030513 - 18 Jan 2023

Cited by 1 | Viewed by 2052

Abstract

Cloud computing and its associated virtualization have already been the most vital architectures in the current computer system design. Due to the popularity and progress of cloud computing in different organizations, performance evaluation of cloud computing is particularly significant, which helps computer designers make plans for the system’s capacity. This paper aims to evaluate the performance of a cloud datacenter Bitbrains, using a queueing model only from CPU utilization data. More precisely, a simple but non-trivial queueing model is used to represent the task processing of each virtual machine (VM) in the cloud, where the input stream is supposed to follow a non-homogeneous Poisson process (NHPP). Then, the parameters of arrival streams for each VM in the cloud are estimated. Furthermore, the superposition of estimated arrivals is applied to represent the CPU behavior of an integrated virtual platform. Finally, the performance of the integrated virtual platform is evaluated based on the superposition of the estimations. Full article

(This article belongs to the Special Issue Advances in Data Mining, Machine Learning and Causal Inference and Their Applications)

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15 pages, 1544 KiB

Open AccessArticle

Time-Varying Sequence Model

by Sneha Jadhav, Jianxiang Zhao, Yepeng Fan, Jingjing Li, Hao Lin, Chenggang Yan and Minghan Chen

Mathematics 2023, 11(2), 336; https://doi.org/10.3390/math11020336 - 9 Jan 2023

Cited by 3 | Viewed by 1759

Abstract

Traditional machine learning sequence models, such as RNN and LSTM, can solve sequential data problems with the use of internal memory states. However, the neuron units and weights are shared at each time step to reduce computational costs, limiting their ability to learn time-varying relationships between model inputs and outputs. In this context, this paper proposes two methods to characterize the dynamic relationships in real-world sequential data, namely, the internal time-varying sequence model (ITV model) and the external time-varying sequence model (ETV model). Our methods were designed with an automated basis expansion module to adapt internal or external parameters at each time step without requiring high computational complexity. Extensive experiments performed on synthetic and real-world data demonstrated superior prediction and classification results to conventional sequence models. Our proposed ETV model is particularly effective at handling long sequence data. Full article

(This article belongs to the Special Issue Advances in Data Mining, Machine Learning and Causal Inference and Their Applications)

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19 pages, 1308 KiB

Open AccessArticle

Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base

by Jiakang Xu, Wolfgang Mayer, Hongyu Zhang, Keqing He and Zaiwen Feng

Mathematics 2022, 10(24), 4778; https://doi.org/10.3390/math10244778 - 15 Dec 2022

Cited by 3 | Viewed by 1347

Abstract

A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem (Rel2Onto). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach for learning the semantics of a data source is desirable. Most of the existing work studies the semantic annotation of source attributes. However, although critical, the research for automatically inferring the relationships between attributes is very limited. In this paper, we propose a novel method for semantically annotating structured data sources using machine learning, graph matching and modified frequent subgraph mining to amend the candidate model. In our work, Knowledge graph is used as prior knowledge. Our evaluation shows that our approach outperforms two state-of-the-art solutions in tricky cases where only a few semantic models are known. Full article

(This article belongs to the Special Issue Advances in Data Mining, Machine Learning and Causal Inference and Their Applications)

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