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Interpretable cost-sensitive regression through one-step boosting
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2023 | Decision Support Systems | Citations: 0
Authors: Decorte, Thomas; Raymaekers, Jakob; Verdonck, Tim

Abstract: In most practical prediction problems, such as regression and classification, th ... Expand

Abstract: In most practical prediction problems, such as regression and classification, the different types of prediction errors are not equally costly in the decision-making process. Although there exist numerous real-world costsensitive regression problems, ranging from loan charge-off forecasting to house price predictions, the literature on cost-sensitive learning mainly focuses on classification and only a few solutions are proposed for regression problems. These regressions are typically characterized by an asymmetric cost structure, where over- and underpredictions of a similar magnitude face vastly different costs. In this paper, we present a one-step boosting method (OSB) for cost-sensitive regression. The proposed methodology leverages a secondary learner to incorporate cost-sensitivity into an already trained cost-insensitive regression model. The secondary learner is defined as a linear function of certain variables deemed interesting for cost-sensitivity. These variables do not necessarily need to be the same as in the already trained model. An efficient optimization algorithm is achieved through iteratively reweighted least squares using the asymmetric cost function. The obtained results become interpretable through bootstrapping, enabling decision makers to distinguish important variables for cost-sensitivity as well as facilitating statistical inference. Applying different cost functions and various initial cost-insensitive learning methods on several public datasets consistently yields a significant reduction in the average misprediction cost, illustrating the excellent performance of our approach. Collapse

Semantic filters: Keras

Topics: Python decision making ease of use operating system Microsoft Windows

Methods: computational algorithm regression analysis method LightGBM boosting method linear regression analysis
Near-Infrared Spectroscopy for Bladder Monitoring: A Machine Learning Approach
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2023 | ACM Transactions on Management Information Systems | Citations: 0
Authors: Fechner, Pascal; König, Fabian; Kratsch, Wolfgang; Lockl, Jannik; Röglinger, Maximilian

Abstract: Patients living with neurogenic bladder dysfunction can lose the sensation of th ... Expand

Abstract: Patients living with neurogenic bladder dysfunction can lose the sensation of their bladder filling. To avoid over-distension of the urinary bladder and prevent long-term damage to the urinary tract, the gold standard treatment is clean intermittent catheterization at predefined time intervals. However, the emptying schedule does not consider actual bladder volume, meaning that catheterization is performed more often than necessary, which can lead to complications such as urinary tract infections. Time-consuming catheterization also interferes with patients' daily routines and, in the case of an empty bladder, uses human and material resources unnecessarily. To enable individually tailored and volume-responsive bladder management, we design a model for the continuous monitoring of bladder volume. During our design science research process, we evaluate the model's applicability and usefulness through interviews with affected patients, prototyping, and application to a real-world in vivo dataset. The developed prototype predicts bladder volume based on relevant sensor data (i.e., near-infrared spectroscopy and acceleration) and the time elapsed since the previous micturition. Our comparison of several supervised state-of-the-art machine and deep learning models reveals that a long short-term memory network architecture achieves a mean absolute error of 116.7 ml that can improve bladder management for patients. Collapse

Semantic filters: Keras

Topics: computer hardware open source mobile application user interface programming language

Methods: design artifact qualitative interview design process support vector machine computational algorithm
What Do They Meme? Exploring the Role of Memes as Cultural Symbols of Online Communities

2023 | International Conference on Information Systems | Citations: 0
Authors: Henn, Theresa; Posegga, Oliver

Abstract: Analyzing symbols shared within online communities (OCs) is essential to better ... Expand

Abstract: Analyzing symbols shared within online communities (OCs) is essential to better understand communities' expressed cultures. To evaluate how OCs differ in their expressed culture and analyze the effects of community rules (CR) and moderation policies (MP), we examined meme sharing of subreddit and interaction communities on Reddit. To detect memes shared within subreddits automatically, we trained a convolutional neural network and applied a feature-matching algorithm to create meme networks with components consisting of visually similar memes. Based on each community's component composition, we created community-specific meme languages that we compared across subreddit and interaction communities. Our results show that memes can be aggregated to characteristic meme languages linked to individual OCs; yet MP and CR do not impact the homogeneity of shared memes. Based on these findings, we plan to analyze dynamically the relationship between memes and OCs, examining memes' textual content and diving deeper into users' individual meme languages. Collapse

Semantic filters: Keras

Topics: Reddit internet technology social media

Methods: computational algorithm nonparametric test Mann Whitney U test convolutional neural network Keras
Pictures that are Worth a Thousand Donations: How Emotions in Project Images Drive the Success of Online Charity Fundraising Campaigns? An Image Design Perspective

2023 | Management Information Systems Quarterly | Citations: 0
Authors: Hou, Jian-Ren; Zhang, Jie; Zhang, Kunpeng

Abstract: Charity fundraising is becoming increasingly reliant on online platforms such a ... Expand

Abstract: Charity fundraising is becoming increasingly reliant on online platforms such as crowdfunding platforms. However, overwhelmingly, crowdfunding campaigns are not meeting their goals. Therefore, it is imperative to examine how the success of charity fundraising campaigns can be improved. In this paper, we focus on the design of project images on a crowdfunding website, which portray the themes and content of the projects. Employing the stimulus-organism-response (S-O-R) model, we investigate the relationships between image attributes (S) and image emotions (O), and between image emotions (O) and campaign outcomes (R). We developed and trained a deep neural network model to identify the emotions conveyed in the images, and then implemented it to analyze project images from a popular crowdfunding platform. We applied the obtained image emotions together with the objective image attributes and the project outcome metrics to explore from a design perspective, what image attributes evoke image emotions, and how image emotions are related to the success of charity fundraising projects. Our results confirm these relationships and further suggest that the roles of image emotions on the success of crowdfunding campaigns vary with project characteristics such as the project budget and category. In addition, the image emotions of competing projects on the crowdfunding platform were found to reduce the project's performance. In an extended study, we conducted an online randomized controlled experiment by manipulating image attributes to reexamine the causal relationships and verify the mediating roles of positive and negative empathies between image emotions and campaign outcomes. This research contributes to the charity fundraising literature from a novel perspective of emotions in project images. It presents new and unique findings regarding the mediation roles of positive and negative empathies and the limitations of the emotion of sadness in certain types of charity fundraising. In addition, our findings provide useful insights for practitioners seeking to design successful online charity campaigns. Collapse

Semantic filters: Keras

Topics: crowdsourcing crowdfunding project productivity Google+ website

Methods: experiment deep learning computational algorithm machine learning descriptive statistic
LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans
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2023 | ACM Transactions on Management Information Systems | Citations: 0
Authors: Joshi, Amogh Manoj; Nayak, Deepak Ranjan; Das, Dibyasundar; Zhang, Yudong

Abstract: Recent years have witnessed a rise in employing deep learning methods, especiall ... Expand

Abstract: Recent years have witnessed a rise in employing deep learning methods, especially convolutional neural networks (CNNs) for detection of COVID-19 cases using chest CT scans. Most of the state-of-the-art models demand a huge amount of parameters which often suffer from overfitting in the presence of limited training samples such as chest CT data and thereby, reducing the detection performance. To handle these issues, in this paper, a lightweight multi-scale CNN called LiMS-Net is proposed. The LiMS-Net contains two feature learning blocks where, in each block, filters of different sizes are applied in parallel to derive multi-scale features from the suspicious regions and an additional filter is subsequently employed to capture discriminant features. The model has only 2.53M parameters and therefore, requires low computational cost and memory space when compared to pretrained CNN architectures. Comprehensive experiments are carried out using a publicly available COVID-19 CT dataset and the results demonstrate that the proposed model achieves higher performance than many pretrained CNN models and state-of-the-art methods even in the presence of limited CT data. Our model achieves an accuracy of 92.11% and an F1-score of 92.59% for detection of COVID-19 from CT scans. Further, the results on a relatively larger CT dataset indicate the effectiveness of the proposed model. Collapse

Semantic filters: Keras

Topics: covid 19

Methods: computed tomography ImageNet ablation analysis experiment machine learning
Diffusion of real versus misinformation during a crisis event: A big data-driven approach
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2023 | International Journal of Information Management | Citations: 0
Authors: King, Kelvin K.; Wang, Bin

Abstract: Misinformation has captured the interest of academia in recent years with severa ... Expand

Abstract: Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly with inconsistent results. In this research, we attempt to bridge the gap in the literature by examining the impacts of user-, time-, and content-based characteristics that affect the virality of real versus misinformation during a crisis event. Using a big data-driven approach, we collected over 42 million tweets during Hurricane Harvey and obtained 3589 original verified real or false tweets by cross-checking with fact-checking websites and a relevant federal agency. Our results show that virality is higher for misinformation, novel tweets, and tweets with negative sentiment or lower lexical density. In addition, we reveal the opposite impacts of sentiment on the virality of real news versus misinformation. We also find that tweets on the environment are less likely to go viral than the baseline religious news, while real social news tweets are more likely to go viral than misin formation on social news. Collapse

Semantic filters: Keras

Topics: Twitter misinformation social media hurricane big data

Methods: machine learning computational algorithm natural language processing decision tree classification gradient boosting
Visualizing Convolutional Neural Network Models’ Sensitivity to Nonnatural Data Order
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2023 | Information Systems Frontiers | Citations: 0
Authors: Klepetko, Randy; Krishnan, Ram

Abstract: Convolutional neural networks (CNN) have revolutionized image recognition techno ... Expand

Abstract: Convolutional neural networks (CNN) have revolutionized image recognition technology and found applications in various nonimage-related fields. For nonnatural data, such as cybersecurity packets, in which the data sample order is not defined by nature, some models trained on certain orderings of data perform better than when trained with other orderings. Some orderings create patterns from which the CNN extracts better features. Understanding how to best order the training data to improve CNN performance is not well-studied. In this study, we investigate this problem by examining malware infections using different CNN models and include visualization tools to enhance our analysis. We define a functional algorithm for ordering and show that order importance in CNNs is model dependent. In addition, we show that depending on the model, statistical relationships are crucial in establishing order with better performance. Collapse

Semantic filters: Keras

Topics: malware mobile system cloud computing infrastructure as a service

Methods: experiment descriptive statistic machine learning computational algorithm Keras
Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster
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2023 | Information Systems Frontiers | Citations: 0
Authors: Kumar, Abhinav; Singh, Jyoti Prakash; Rana, Nripendra P.; Dwivedi, Yogesh K.

Abstract: During a disaster, a large number of disaster-related social media posts are wid ... Expand

Abstract: During a disaster, a large number of disaster-related social media posts are widely disseminated. Only a small percentage of disaster-related information is posted by eyewitnesses. The post of a disaster eyewitness offers an accurate depiction of the disaster. Therefore, the information posted by the eyewitness is preferred over the other source of information as it is more effective at helping organize rescue and relief operations and potentially saving lives. In this work, we propose a multi-channel convolutional neural network (MCNN) that uses three different word-embedding vectors together to classify disaster-related tweets into eyewitness, non-eyewitness, and don’t know classes. We compared the performance of the proposed multi-channel convolutional neural network with several attention-based deep-learning models and conventional machine learning-models such as recurrent neural network, gated recurrent unit, Long-Short-Term-Memory, convolutional neural network, logistic regression, support vector machine, and gradient boosting. The proposed multi-channel convolutional neural network achieved an F1-score of 0.84, 0.88, 0.84, and 0.86 with four disaster-related datasets of floods, earthquakes, hurricanes, and wildfires, respectively. The experimental results show that the training MCNN model with different word embedding together performs better than the conventional machine-learning models and several other deep-learning models. Collapse

Semantic filters: Keras

Topics: Twitter earthquake hurricane crowdsourcing social media

Methods: machine learning deep learning convolutional neural network experiment GloVe
Use of Natural Language Processing Techniques in the Construct and Instrument Development Process

2023 | European Conference On Information Systems | Citations: 0
Authors: Larsen, Kai; Sharma, Rajeev; Queiroz, Magno; Arnulf, Jan; Pillet, Jean-Charles

Abstract: The construct and instrument development process relies significantly on human ... Expand

Abstract: The construct and instrument development process relies significantly on human judgment in the initial stages of the process, specifically in developing construct definition statements, and in developing measurement instruments with high content validity. Natural language processing (NLP) techniques can be used to support human judgment and improve the quality of constructs and instruments employed in research. This paper describes the use of NLP techniques in the construct and instrument development process and presents illustrative results from the use of those techniques. We develop an NLP-based algorithm and illustrate its use to assess measurement instruments. The data used to train the algorithm was 37 years of constructs published in premier IS journals during the period of 1980-2016. The empirical illustrations support our premise that the use of NLP techniques can improve the rigor of the process and improve the quality of constructs and instruments employed in research. Collapse

Semantic filters: Keras

Topics: website ease of use application programming interface perceived usefulness perceived ease of use

Methods: natural language processing survey design computational algorithm machine learning survey
Improved learning performance for small datasets in high dimensions by new dual-net model for non-linear interpolation virtual sample generation
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2023 | Decision Support Systems | Citations: 0
Authors: Lin, Liang-Sian; Lin, Yao-San; Li, Der-Chiang; Liu, Yun-Hsuan

Abstract: The number of reliable samples obtained in early decision-making activity is usu ... Expand

Abstract: The number of reliable samples obtained in early decision-making activity is usually relatively small. Due to variable distribution and incomplete structure of tiny datasets, it is challenging to create reliable and robust predictive modeling using classic statistical and machine learning models in small sample settings. The virtual sample generation (VSG) technique improves model learning accuracies for minimal datasets across diverse applications. Virtual samples on independent variables were generated using established VSG methods predi cated on the assumption of a probability distribution or a membership function to fill data gaps. However, in the actual world, non-linear function interactions between variables are common. To address this issue, this paper developed a novel VSG method called Dual-VSG, which generates non-linear interpolation virtual samples using a self-supervised learning (SSL) framework to improve learning performance on small datasets. We generated non-linear interpolation virtual samples without labels by estimating non-linear functions and transforming them into a high-dimensional space using the proposed dual-net model. The weights of the dual-net model are transferred to a downstream task to generate virtual sample labels. To demonstrate the effectiveness of the suggested strategy, this research employed five datasets. On the Backpropagation Neural Networks (BPNN) predictive model, we compared the suggested method’s prediction performance to two state-of-the-art VSG approaches. To assess prediction performance on a regression dataset, the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) are used. Furthermore, the classification accuracy (ACC) and the Fl measure are used to assess classification capability on classification datasets. In addition, the paired t-test was utilized to see if the suggested Dual-VSG approach differed significantly from the other VSG methods in terms of RMSE, MAPE, accuracy (ACC), or F1 score. For short datasets, the suggested Dual-VSG method out performs those VSG methods, according to our experimental results. Collapse

Semantic filters: Keras

Topics: Python programming language

Methods: data transformation t-distributed stochastic neighbor embedding experiment machine learning parametric test

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Found 91 articles

Interpretable cost-sensitive regression through one-step boosting

Near-Infrared Spectroscopy for Bladder Monitoring: A Machine Learning Approach

What Do They Meme? Exploring the Role of Memes as Cultural Symbols of Online Communities

Pictures that are Worth a Thousand Donations: How Emotions in Project Images Drive the Success of Online Charity Fundraising Campaigns? An Image Design Perspective

LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans

Diffusion of real versus misinformation during a crisis event: A big data-driven approach

Visualizing Convolutional Neural Network Models’ Sensitivity to Nonnatural Data Order

Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster

Use of Natural Language Processing Techniques in the Construct and Instrument Development Process

Improved learning performance for small datasets in high dimensions by new dual-net model for non-linear interpolation virtual sample generation