Results

Found 7 articles

Clear filters

Multicategory choice modeling with sparse and high dimensional data: A Bayesian deep learning approach
PDF
2022 | Decision Support Systems | Citations: 1
Authors: Xia, Feihong; Chatterjee, Rabikar

Abstract: The availability of sparse and high dimensional consumer shopping data poses a c ... Expand

Abstract: The availability of sparse and high dimensional consumer shopping data poses a challenge for researchers for accurate and efficient analysis. While deep learning models can handle such data, most of the results from these models are uninterpretable. This greatly limits their value in applications aimed at better understanding mul ticategory shopping behavior and assisting managerial decision-making. Thus, a new approach is needed to analyze high dimensional consumer shopping data in an efficient and interpretable manner, with the potential to greatly strengthen decision support systems for managers especially at large consumer packaged goods firms and giant retailers. We propose a Bayesian deep learning approach based on Variational Autoencoder (VAE) designed to efficiently capture often complex and multifaceted interrelationships across items in the shopping baskets, in the form of substitute and complementary cross effects (which are observable and controllable) and coincidences (which are not). The benefits of the proposed approach are empirically demonstrated by applying our model to a high dimensional supermarket shopping dataset covering very large numbers of products at the stock-keeping unit (SKU) level, households, and shopping trips. We discuss implications for managerial decision-making and identify promising research directions. Collapse

Semantic filters: restricted boltzmann machine

Topics: marketing management decision making open source accounting decision support system

Methods: artificial neural network machine learning deep learning data transformation factor analysis
A Data-driven Characterization of Modern Android Spyware
PDF
2020 | ACM Transactions on Management Information Systems | Citations: 0
Authors: Pierazzi, Fabio; Mezzour, Ghita; Han, Qian; Colajanni, Michele; Subrahmanian, V. S.

Abstract: According to Nokia’s 2017 Threat Intelligence Report, 68.5% of malware targets t ... Expand

Abstract: According to Nokia’s 2017 Threat Intelligence Report, 68.5% of malware targets the Android platform; Windows is second with 28%, followed by iOS and other platforms with 3.5%. The Android spyware family UAPUSH was responsible for the most infections, and several of the top 20 most common Android malware were spyware. Simply put, modern spyware steals the basic information needed to fuel more deadly attacks such as ransomware and banking fraud. Not surprisingly, some forms of spyware are also classified as banking trojans (e.g., ACECARD). We present a data-driven characterization of the principal factors that distinguish modern Android spyware (July 2016–July 2017) both from goodware and other Android malware, using both traditional and deep ML. First, we propose an Ensemble Late Fusion (ELF) architecture that combines the results of multiple classifiers’ predicted probabilities to generate a final prediction. We show that ELF outperforms several of the best-known traditional and deep learning classifiers. Second, we automatically identify key features that distinguish spyware both from goodware and from other malware. Finally we present a detailed analysis of the factors distinguishing five important families of Android spyware: UAPUSH, PINCER, HEHE, USBCLEAVER, and ACECARD (the last is a hybrid spyware-banking trojan). Collapse

Semantic filters: restricted boltzmann machine

Topics: spyware malware Android mobile application data breach

Methods: machine learning deep learning computational algorithm decision tree classification experiment
Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE
PDF
2020 | Information Systems Frontiers | Citations: 2
Authors: Smiti, Salima; Soui, Makram

Abstract: Imbalanced classification on bankruptcy prediction is considered as one of the m ... Expand

Abstract: Imbalanced classification on bankruptcy prediction is considered as one of the most important topics in financial institutions. In this context, various statistical and artificial intelligence methods have been proposed. Recently, deep learning algorithms are experiencing a resurgence of interest, and are widely used to build a prediction and classification models. To this end, we propose a novel deep learning-based approach called BSM-SAES. This approach combines Borderline Synthetic Minority oversampling technique (BSM) and Stacked AutoEncoder (SAE) based on the Softmax classifier. The aim is to develop an accurate and reliable bankruptcy prediction model which includes the features extraction process. To assess the classification performance of our proposed model, k- nearest neighbor, decision tree, support vector machine, and artificial neural network, C5.0 that are machine learning methods, are applied. We evaluate our proposed approach on the Polish imbalanced datasets. The obtained results confirm the efficiency of our proposed model compared to other machine learning models regarding predicting and classifying the financial status of a firm. Collapse

Semantic filters: restricted boltzmann machine

Topics: decision making organizational productivity

Methods: computational algorithm autoencoder machine learning data transformation deep learning
E-Commerce Product Categorization via Machine Translation
PDF
2020 | ACM Transactions on Management Information Systems | Citations: 0
Authors: Tan, Liling; Li, Maggie Yundi; Kok, Stanley

Abstract: E-commerce platforms categorize their products into a multi-level taxonomy tree ... Expand

Abstract: E-commerce platforms categorize their products into a multi-level taxonomy tree with thousands of leaf categories. Conventional methods for product categorization are typically based on machine learning classification algorithms. These algorithms take product information as input (e.g., titles and descriptions) to classify a product into a leaf category. In this article, we propose a new paradigm based on machine translation. In our approach, we translate a product’s natural language description into a sequence of tokens representing a root-to-leaf path in a product taxonomy. In our experiments on two large real-world datasets, we show that our approach achieves better predictive accuracy than a state-of-the-art classification system for product categorization. In addition, we demonstrate that our machine translation models can propose meaningful new paths between previously unconnected nodes in a taxonomy tree, thereby transforming the taxonomy into a directed acyclic graph. We discuss how the resultant taxonomy directed acyclic graph promotes user-friendly navigation, and how it is more adaptable to new products. Collapse

Semantic filters: restricted boltzmann machine

Topics: electronic commerce

Methods: machine learning transformer model experiment machine translation restricted boltzmann machine
Deep Learning for Improved Agricultural Risk Management

2019 | HICSS | Citations: 0
Authors: Newlands, Nathaniel K; Ghahari, Azar

Abstract: Deep learning provides many beneﬁts, including automation, speed, accuracy, and ... Expand

Abstract: Deep learning provides many beneﬁts, including automation, speed, accuracy, and intelligence, and it is delivering competitive performance now across a wide range of real-world operational applications – from credit card fraud detection to recommender systems and customer segmentation. Its potential in actuarial sciences and agricultural insurance/risk management, however, remains largely untapped. In this pilot study, we investigate deep learning in predicting agricultural yield in time and space under weather/climate uncertainty. We evaluate the predictive power of deep learning, benchmarking its performance against more conventional approaches alongside both weather station and climate. Our ﬁndings reveal that deep learning offers the highest predictive accuracy, outperforming all the other approaches. We infer that it also has great potential to reduce underwriting inefﬁciencies and insurance coverage costs associated with using more imprecise yield-based metrics of real risk exposure. Future work aims to further evaluate its performance, from municipal area-yield, to ﬁner-scale crop-speciﬁc producer-scale yield. Collapse

Semantic filters: restricted boltzmann machine

Topics: information system use

Methods: deep learning computational algorithm principal component analysis data transformation machine learning
Combining Geographical and Social Influences with Deep Learning for Personalized Point-of-Interest Recommendation
PDF
2018 | Journal of Management Information Systems | Citations: 0
Authors: Guo, Junpeng; Zhang, Wenxiang; Fan, Weiguo; Li, Wenhua

Abstract: Personalized point-of-interest (POI) recommendation is important to location-bas ... Expand

Abstract: Personalized point-of-interest (POI) recommendation is important to location-based social networks (LBSNs) for helping users to explore new places and for helping third-party services to launch targeted advertisements. Discovering effective features or representations from check-in data is the key to POI recommendation. Deep learning is a representation-learning method with multiple levels for discovering intrinsic features to better represent user preferences. We analyzed users' check-in behavior in detail and developed a deep learning model to integrate geographical and social influences for POI recommendation tasks. We used a semi-restricted Boltzmann machine to model the geographical similarity and a conditional layer to model the social influence. Experiments with real-world LBSNs showed that our method performed better than other state-of-the-art methods. Theoretically, our study contributes to the effective usage of data science and analytics for social recommender system design. In practice, our results can be used to improve the quality of personalized POI recommendation services for websites and applications. Collapse

Semantic filters: restricted boltzmann machine

Topics: social influence geographic information system missing data recommender system systems design

Methods: deep learning computational algorithm experiment term frequency–inverse document frequency design science
A Hybrid Method for Cross-domain Sentiment Classification Using Multiple Sources

2012 | International Conference on Information Systems | Citations: 0
Authors: Fang, Fang; Datta, Anindya; Dutta, Kaushik

Abstract: Sentiment classification is one of the most extensively studied problems in sent ... Expand

Abstract: Sentiment classification is one of the most extensively studied problems in sentiment analysis and supervised learning methods, which require labeled data for training, have been proven quite effective. However, supervised methods assume that the training domain and the testing domain share the same distribution; otherwise, accuracy drops dramatically. Although this does not pose problems when training data are readily available, in some circumstances, labeled data is quite expensive to acquire. For instance, if we want to detect sentiment from Tweets or Facebook comments, the only way to acquire is to manually label it and thus prohibitively burdensome and timeconsuming. In this paper, we propose a hybrid approach that integrates the sentiment information from multiple source domains labeled data and a set of preselected sentiment words to solve this problem. The experimental results suggest that our method statistically outperforms the state of the art and even surpasses the in-domain gold standard in some cases. Collapse

Semantic filters: restricted boltzmann machine

Topics: Apple iPhone accounting social network blog

Methods: support vector machine restricted boltzmann machine natural language processing sentiment analysis literature study

Page 1 of 1

AIS11

Journals

Conferences

Semantic Filter

Found 7 articles

Multicategory choice modeling with sparse and high dimensional data: A Bayesian deep learning approach

A Data-driven Characterization of Modern Android Spyware

Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE

E-Commerce Product Categorization via Machine Translation

Deep Learning for Improved Agricultural Risk Management

Combining Geographical and Social Influences with Deep Learning for Personalized Point-of-Interest Recommendation

A Hybrid Method for Cross-domain Sentiment Classification Using Multiple Sources