Interpretable cost-sensitive regression through one-step boosting

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

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

Methods: computational algorithm regression analysis method LightGBM boosting method linear regression analysis

ROLEX: A Novel Method for Interpretable Machine Learning Using Robust Local Explanations

2023 | Management Information Systems Quarterly | Citations: 2

Authors: Kim, Buomsoo (Raymond); Srinivasan, Karthik; Kong, Sung Hye; Kim, Jung Hee; Shin, Chan Soo; Ram, Sudha

Abstract: Recent developments in big data technologies are revolutionizing the field of h ... Expand

Abstract: Recent developments in big data technologies are revolutionizing the field of healthcare predictive analytics (HPA), enabling researchers to explore challenging problems using complex prediction models. Nevertheless, healthcare practitioners are reluctant to adopt those models as they are less transparent and accountable due to their black-box structure. We believe that instance-level, or local, explanations enhance patient safety and foster trust by enabling patient-level interpretations and medical knowledge discovery. Therefore, we propose the RObust Local EXplanations (ROLEX) method to develop robust, instance-level explanations for HPA models in this study. ROLEX adapts state-of-the-art methods and ameliorates their shortcomings in explaining individual-level predictions made by black-box machine learning models. Our analysis with a large real-world dataset related to a prevalent medical condition called fragility fracture and two publicly available healthcare datasets reveals that ROLEX outperforms widely accepted benchmark methods in terms of local faithfulness of explanations. In addition, ROLEX is more robust since it does not rely on extensive hyperparameter tuning or heuristic algorithms. Explanations generated by ROLEX, along with the prototype user interface presented in this study, have the potential to promote personalized care and precision medicine by providing patient-level interpretations and novel insights. We discuss the theoretical implications of our study in healthcare, big data, and design science. Collapse

Topics: explainable artificial intelligence usability user interface local interpretable model agnostic explanation problem solving

Methods: machine learning design artifact computational algorithm longitudinal research ablation analysis

Theories: lemon market theory

Tragedy, Truth, and Technology: The 3T Theory of Social Media-Driven Misinformation

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Roberts, Nicholas; Qahri-Saremi, Hamed

Abstract: False claims and misinformation on social media have substantially increased in ... Expand

Abstract: False claims and misinformation on social media have substantially increased in the last few years. Some people have incorrectly interpreted such content as the truth, sometimes to the extent that it transforms their view of reality and subsequently leads to actions that cause harm and suffering. Despite the burgeoning body of research on misinformation on social media, we know little about the process by which social media-driven misinformation shapes and reinforces false beliefs that result in detrimental outcomes. Building on insights from dramaturgical tragedy, theories of truth, and research on social media, we develop a process theory that explains how social media-driven misinformation transforms a person's view of reality in a way that leads to detrimental human action. Collapse

Topics: social media misinformation anonymity disinformation

Methods: business process modeling theory development boosting method

Theories: correspondence theory of truth

Efficient fraud detection using deep boosting decision trees

2023 | Decision Support Systems | Citations: 0

Authors: Xu, Biao; Wang, Yao; Liao, Xiuwu; Wang, Kaidong

Abstract: Fraud detection is to identify, monitor, and prevent potentially fraudulent acti ... Expand

Abstract: Fraud detection is to identify, monitor, and prevent potentially fraudulent activities from complex data. The recent development and success in AI, especially machine learning, provides a new data-driven way to deal with fraud. From a methodological point of view, machine learning based fraud detection can be divided into two categories, i.e., conventional methods (decision tree, tree boosting methods, etc.) and deep learning, both of which have significant limitations in terms of the lack of representation learning ability for the former and interpretability for the latter. Furthermore, due to the rarity of detected fraud cases, the associated data is usually imbalanced, which seriously degrades the performance of classification algorithms. Collapse

Topics: fraud detection marketing management

Methods: machine learning computational algorithm decision tree classification artificial neural network gradient boosting

A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations

2022 | Decision Support Systems | Citations: 0

Authors: Chen, Chaofan; Lin, Kangcheng; Rudin, Cynthia; Shaposhnik, Yaron; Wang, Sijia; Wang, Tong

Abstract: A B S T R A C TLending decisions are usually made with proprietary models that ... Expand

Abstract: A B S T R A C TLending decisions are usually made with proprietary models that provide minimally acceptable explanations to users. In a future world without such secrecy, what decision support tools would one want to use for justified lending decisions? This question is timely, since the economy has dramatically shifted due to a pandemic, and a massive number of new loans will be necessary in the short term. We propose a framework for such decisions, including a globally interpretable machine learning model, an interactive visualization of it, and several types of summaries and explanations for any given decision. The machine learning model is a two-layer additive risk model, which resembles a two-layer neural network, but is decomposable into subscales. In this model, each node in the first (hidden) layer represents a meaningful subscale model, and all of the nonlinearities are transparent. Our online visualization tool allows exploration of this model, showing precisely how it came to its conclusion. We provide three types of explanations that are simpler than, but consistent with, the global model: case-based reasoning explanations that use neighboring past cases, a set of features that were the most important for the model's prediction, and summary-explanations that provide a customized sparse explanation for any particular lending decision made by the model. Our framework earned the FICO recognition award for the Explainable Machine Learning Challenge, which was the first public challenge in the domain of explainable machine learning. 1 Collapse

Topics: logistics management decision support system explainable artificial intelligence database system missing data

Methods: machine learning artificial neural network computational algorithm optimization model paired t-test

Theories: theory of case-based reasoning

Recognition of human activities for wellness management using a smartphone and a smartwatch: A boosting approach

2021 | Decision Support Systems | Citations: 0

Authors: Tarafdar, Pratik; Bose, Indranil

Abstract: Mobile health applications are considered to be powerful tools for activity-base ... Expand

Abstract: Mobile health applications are considered to be powerful tools for activity-based wellness management. With the availability of multimodal sensors in smart devices used in our daily lives, it is possible to track human activity and deliver context-aware wellness services. The embedded sensors in naturally used devices such as smart­ phones, smartwatches, and wearables contain rich information that can be integrated for human activity recognition. Our research demonstrates how powerful boosting algorithms can extract knowledge for human activity classification in a real-life setting. Our results show that boosting classifiers outperform traditional machine learning classifiers in the detection of basic human activities such as walking, standing, sitting, exercise, and sleeping. Further, we perform feature engineering to compare the potential of a smartphone and a smart­ watch in activity detection. Our feature engineering strategy provides directions about the selection of sensor features for improvement in classification of basic human activities. The theoretical and practical implications of this research for activity-based wellness management are also discussed. Collapse

Topics: smartphone smart watch mobile system smart wearable device smart device

Methods: computational algorithm feature engineering machine learning AdaBoost support vector machine

Assessing product competitive advantages from the perspective of customers by mining user-generated content on social media

2019 | Decision Support Systems | Citations: 0

Authors: Liu, Yao; Jiang, Cuiqing; Zhao, Huimin

Abstract: User-generated content (UGC) is becoming increasingly available on social media ... Expand

Abstract: User-generated content (UGC) is becoming increasingly available on social media for a wide range of products and services. Such UGC contains rich information about customer attitudes, opinions, and experiences. We propose a novel method for product competitive advantage analysis, which provides an essential basis for quality management and marketing strategy development, by mining UGC. Compared to traditional product performance analysis methods based on manufacturers' internal data and expert reviews, our method better reflects the perspective of customers. While a few recent methods based on UGC analysis assess the performance of one product in isolation, our method reveals the competitive advantages (and disadvantages) of a target product relative to its competitors. Our method uses supervised learning to identify competitors from UGC and domain-specific sentiment analysis to quantify customer attitudes. A case study in the automotive industry demonstrates the utility of the method. Collapse

Topics: competitive advantage website word of mouth social media knowledge acquisition

Methods: sentiment analysis case study forums posting logistic regression classification method

The seaport service rate prediction system: Using drayage truck trajectory data to predict seaport service rates

2017 | Decision Support Systems | Citations: 2

Authors: Wasesa, Meditya; Stam, Andries; van Heck, Eric

Abstract: For drayage operators the service rate of seaports is crucial for organizing the ... Expand

Abstract: For drayage operators the service rate of seaports is crucial for organizing their container pick-up/delivery operations. This study presents a seaport service rate prediction system that could help drayage operators to improve their predictions of the duration of the pick-up/delivery operations at a seaport by using the subordinate trucks' trajectory data. The system is constructed based on three components namely, trajectory reconstruction, geo-fencing analysis, and gradient boosting modelling. Using predictive analytic techniques, the prediction system is trained and validated using more than 15million data records from over 200 trucks over a period of 19months. The gradient boosting model-based solution provides better predictions compared with the linear model benchmark solution. Conclusions and implications are formulated. Collapse

Topics: database system data quality anonymity decision making wireless computer network

Methods: gradient boosting machine learning descriptive statistic case study generalized linear model

Weighted subspace modeling for semantic concept retrieval using gaussian mixture models

2016 | Information Systems Frontiers | Citations: 3

Authors: Chen, Chao; Shyu, Mei-Ling; Chen, Shu-Ching

Abstract: At the era of digital revolution, social media data are growing at an explosive ... Expand

Abstract: At the era of digital revolution, social media data are growing at an explosive speed. Thanks to the prevailing popularity of mobile devices with cheap costs and high resolutions as well as the ubiquitous Internet access provided by mobile carriers, Wi-Fi, etc., numerous numbers of videos and pictures are generated and uploaded to social media websites such as Facebook, Flickr, and Twitter everyday. To efficiently and effectively search and retrieve information from the large amounts of multimedia data (structured, semi-structured, or unstructured), lots of algorithms and tools have been developed. Among them, a variety of data mining and machine learning methods have been explored and proposed and have shown their effectiveness and potentials in handling the growing requests to retrieve semantic information from those large-scale multimedia data. However, it is well-acknowledged that the performance of such multimedia semantic information retrieval is far from satisfactory, due to the challenges like rare events, data imbalance, etc. In this paper, a novel weighted subspace modeling framework is proposed that is based on the Gaussian Mixture Model (GMM) and is able to effectively retrieve semantic concepts, even from the highly imbalanced datasets. Experimental results performed on two public-available benchmark datasets against our previous GMM-based subspace modeling method and the other prevailing counterparts demonstrate the effectiveness of the proposed weighted GMM-based subspace modeling framework with the improved retrieval performance in terms of the mean average precision (MAP) values. Collapse

Topics: website Flickr

Methods: conceptual modelling design science experiment support vector machine decision tree classification

Cost-sensitive and ensemble-based prediction model for outsourced software project risk prediction

2015 | Decision Support Systems | Citations: 0

Authors: Hu, Yong; Feng, Bin; Mo, Xizhu; Zhang, Xiangzhou; Ngai, E. W. T.; Fan, Ming; Liu, Mei

Abstract: Nowadays software is mainly developed through outsourcing and it has become one ... Expand

Abstract: Nowadays software is mainly developed through outsourcing and it has become one of the most important business practice strategies for the software industry. However, outsourcing projects are often affiliated with high failure rate. Therefore to ensure success in outsourcing projects, past research has aimed to develop intelligent risk prediction models to evaluate the success rate and cost-effectiveness of software projects. In this study, we first summarized related work over the past 20years and observed that all existing prediction models assume equal misclassification costs, neglecting actual situations in the management of software projects. In fact, overlooking project failure is far more serious than the misclassification of a success-prone project as a failure. Moreover, ensemble learning, a technique well-recognized to improve prediction performance in other fields, has not yet been comprehensively studied in software project risk prediction. This study aims to close the research gaps by exploring cost-sensitive analysis and classifier ensemble methods. Comparative analysis with T-test on 60 different risk prediction models using 327 outsourced software project samples suggests that the ideal model is a homogeneous ensemble model of decision trees (DT) based on bagging. Interestingly, DT underperformed Support Vector Machine (SVM) in accuracy (i.e., assuming equal misclassification cost), but outperformed in cost-sensitive analysis under the proposed framework. In conclusion, this study proposes the first cost-sensitive and ensemble-based hybrid modeling framework (COSENS) for software project risk prediction. In addition, it establishes a new rigorous evaluation standard for assessing software risk prediction models by considering misclassification costs. Collapse

Topics: IT project IS management

Methods: Student's t-test parametric test experiment support vector machine ensemble learning