| 1. |
- Sweidan, Dirar, et al.
(författare)
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Predicting Customer Churn in Retailing
- 2022
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Ingår i: Proceedings 21st IEEE International Conference on Machine Learning and Applications ICMLA 2022. - : IEEE. - 9781665462839 - 9781665462846 ; , s. 635-640
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Konferensbidrag (refereegranskat)abstract
- Customer churn is one of the most challenging problems for digital retailers. With significantly higher costs for acquiring new customers than retaining existing ones, knowledge about which customers are likely to churn becomes essential. This paper reports a case study where a data-driven approach to churn prediction is used for predicting churners and gaining insights about the problem domain. The real-world data set used contains approximately 200 000 customers, describing each customer using more than 50 features. In the pre-processing, exploration, modeling and analysis, attributes related to recency, frequency, and monetary concepts are identified and utilized. In addition, correlations and feature importance are used to discover and understand churn indicators. One important finding is that the churn rate highly depends on the number of previous purchases. In the segment consisting of customers with only one previous purchase, more than 75% will churn, i.e., not make another purchase in the coming year. For customers with at least four previous purchases, the corresponding churn rate is around 25%. Further analysis shows that churning customers in general, and as expected, make smaller purchases and visit the online store less often. In the experimentation, three modeling techniques are evaluated, and the results show that, in particular, Gradient Boosting models can predict churners with relatively high accuracy while obtaining a good balance between precision and recall.
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| 2. |
- Huhnstock, Nikolas Alexander, 1988-, et al.
(författare)
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An Infinite Replicated Softmax Model for Topic Modeling
- 2019
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Ingår i: Modeling Decisions for Artificial Intelligence. - Cham : Springer. - 9783030267728 - 9783030267735 ; , s. 307-318
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Konferensbidrag (refereegranskat)abstract
- In this paper, we describe the infinite replicated Softmax model (iRSM) as an adaptive topic model, utilizing the combination of the infinite restricted Boltzmann machine (iRBM) and the replicated Softmax model (RSM). In our approach, the iRBM extends the RBM by enabling its hidden layer to adapt to the data at hand, while the RSM allows for modeling low-dimensional latent semantic representation from a corpus. The combination of the two results is a method that is able to self-adapt to the number of topics within the document corpus and hence, renders manual identification of the correct number of topics superfluous. We propose a hybrid training approach to effectively improve the performance of the iRSM. An empirical evaluation is performed on a standard data set and the results are compared to the results of a baseline topic model. The results show that the iRSM adapts its hidden layer size to the data and when trained in the proposed hybrid manner outperforms the base RSM model.
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| 3. |
- Bae, Juhee, et al.
(författare)
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Visual Data Analysis
- 2019
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Ingår i: Data science in Practice. - Cham : Springer. - 9783319975566 - 9783319975559 ; , s. 133-155
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Bokkapitel (refereegranskat)abstract
- Data Science offers a set of powerful approaches for making new discoveries from large and complex data sets. It combines aspects of mathematics, statistics, machine learning, etc. to turn vast amounts of data into new insights and knowledge. However, the sole use of automatic data science techniques for large amounts of complex data limits the human user’s possibilities in the discovery process, since the user is estranged from the process of data exploration. This chapter describes the importance of Information Visualization (InfoVis) and visual analytics (VA) within data science and how interactive visualization can be used to support analysis and decision-making, empowering and complementing data science methods. Moreover, we review perceptual and cognitive aspects, together with design and evaluation methodologies for InfoVis and VA.
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| 4. |
- Peretz-Andersson, Einav, et al.
(författare)
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Empirical AI Transformation Research: A Systematic Mapping Study and Future Agenda
- 2022
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Ingår i: E-Informatica Software Engineering Journal. - : Wroclaw University of Science and Technology. - 2084-4840 .- 1897-7979. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Intelligent software is a significant societal change agent. Recent research indicates that organizations must change to reap the full benefits of AI. We refer to this change as AI transformation (AIT). The key challenge is to determine how to change and which are the consequences of increased AI use. Aim: The aim of this study is to aggregate the body of knowledge on AIT research. Method: We perform an systematic mapping study (SMS) and follow Kitchenham's procedure. We identify 52 studies from Scopus, IEEE, and Science Direct (2010-2020). We use the Mixed-Methods Appraisal Tool (MMAT) to critically assess empirical work. Results Work on AIT is mainly qualitative and originates from various disciplines. We are unable to identify any useful definition of AIT. To our knowledge, this is the first SMS that focuses on empirical AIT research. Only a few empirical studies were found in the sample we identified. Conclusions We define AIT and propose a research agenda. Despite the popularity and attention related to AI and its effects on organizations, our study reveals that a significant amount of publications on the topic lack proper methodology or empirical data.
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| 5. |
- Linnusson, Henrik, et al.
(författare)
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Efficient conformal predictor ensembles
- 2020
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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 397, s. 266-278
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we study a generalization of a recently developed strategy for generating conformal predictor ensembles: out-of-bag calibration. The ensemble strategy is evaluated, both theoretically and empirically, against a commonly used alternative ensemble strategy, bootstrap conformal prediction, as well as common non-ensemble strategies. A thorough analysis is provided of out-of-bag calibration, with respect to theoretical validity, empirical validity (error rate), efficiency (prediction region size) and p-value stability (the degree of variance observed over multiple predictions for the same object). Empirical results show that out-of-bag calibration displays favorable characteristics with regard to these criteria, and we propose that out-of-bag calibration be adopted as a standard method for constructing conformal predictor ensembles.
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| 6. |
- Koriakina, Nadezhda, 1991-, et al.
(författare)
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Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection
- 2024
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 19:4 April
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Tidskriftsartikel (refereegranskat)abstract
- The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Using a microscope, skilled cytotechnologists are able to detect changes due to malignancy; however, introducing this approach into clinical routine is associated with challenges such as a lack of resources and experts. To design a trustworthy OC detection system that can assist cytotechnologists, we are interested in deep learning based methods that can reliably detect cancer, given only per-patient labels (thereby minimizing annotation bias), and also provide information regarding which cells are most relevant for the diagnosis (thereby enabling supervision and understanding). In this study, we perform a comparison of two approaches suitable for OC detection and interpretation: (i) conventional single instance learning (SIL) approach and (ii) a modern multiple instance learning (MIL) method. To facilitate systematic evaluation of the considered approaches, we, in addition to a real OC dataset with patient-level ground truth annotations, also introduce a synthetic dataset—PAP-QMNIST. This dataset shares several properties of OC data, such as image size and large and varied number of instances per bag, and may therefore act as a proxy model of a real OC dataset, while, in contrast to OC data, it offers reliable per-instance ground truth, as defined by design. PAP-QMNIST has the additional advantage of being visually interpretable for non-experts, which simplifies analysis of the behavior of methods. For both OC and PAP-QMNIST data, we evaluate performance of the methods utilizing three different neural network architectures. Our study indicates, somewhat surprisingly, that on both synthetic and real data, the performance of the SIL approach is better or equal to the performance of the MIL approach. Visual examination by cytotechnologist indicates that the methods manage to identify cells which deviate from normality, including malignant cells as well as those suspicious for dysplasia. We share the code as open source.
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| 7. |
- Riveiro, Maria
(författare)
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Evaluation of Uncertainty Visualization Techniques for Information Fusion
- 2007
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Ingår i: 2007 10th International Conference on Information Fusion. - : IEEE. - 9780662458043 - 0662478304 - 9780662478300 ; , s. 623-630
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Konferensbidrag (refereegranskat)abstract
- This paper highlights the importance of uncertainty visualization in information fusion, reviews general methods of representing uncertainty and presents perceptual and cognitive principles from Tufte, Chambers and Bertin as well as users experiments documented in the literature. Examples of uncertainty representations in information fusion are analyzed using these general theories. These principles can be used in future theoretical evaluations of existing or newly developed uncertainty visualization techniques before usability testing with actual users.
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| 8. |
- Ventocilla, Elio, 1984-, et al.
(författare)
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Visual Analytics Solutions as 'off-the-shelf' Libraries
- 2017
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Ingår i: 2017 21st International Conference Information Visualisation (IV). - : IEEE Computer Society. - 9781538608326 - 9781538608319 ; , s. 281-287
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Konferensbidrag (refereegranskat)abstract
- Visual Analytics has brought forward many solutions to different tasks such as exploring topics, understanding user and customer behavior, comparing genomes, or detecting anomalies. Many of these solutions, if not most, are standalone applications with technological contributions which cannot be easily taken for: reuse in other domains, further improvement, benchmarking, or integration and deployment alongside other solutions. The latter can prove specially helpful for exploratory data analysis. This often leads researchers to re-implement solutions and thus to a suboptimal use of skills and resources. This paper discusses further the lack of off-the-shelf libraries for Visual Analytics, and proposes the creation of pluggable libraries on top of existing technologies such as Spark and Zeppelin. We provide an illustrative example of a pluggable, Visual Analytics library using these technologies.
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| 9. |
- Johansson, Ulf, et al.
(författare)
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Conformal Prediction Using Decision Trees
- 2013
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Ingår i: IEEE 13th International Conference on Data Mining (ICDM). - : IEEE Computer Society. - 9780769551081 ; , s. 330-339
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Konferensbidrag (refereegranskat)abstract
- Conformal prediction is a relatively new framework in which the predictive models output sets of predictions with a bound on the error rate, i.e., in a classification context, the probability of excluding the correct class label is lower than a predefined significance level. An investigation of the use of decision trees within the conformal prediction framework is presented, with the overall purpose to determine the effect of different algorithmic choices, including split criterion, pruning scheme and way to calculate the probability estimates. Since the error rate is bounded by the framework, the most important property of conformal predictors is efficiency, which concerns minimizing the number of elements in the output prediction sets. Results from one of the largest empirical investigations to date within the conformal prediction framework are presented, showing that in order to optimize efficiency, the decision trees should be induced using no pruning and with smoothed probability estimates. The choice of split criterion to use for the actual induction of the trees did not turn out to have any major impact on the efficiency. Finally, the experimentation also showed that when using decision trees, standard inductive conformal prediction was as efficient as the recently suggested method cross-conformal prediction. This is an encouraging results since cross-conformal prediction uses several decision trees, thus sacrificing the interpretability of a single decision tree.
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| 10. |
- Johansson, Ulf, et al.
(författare)
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Evolved Decision Trees as Conformal Predictors
- 2013
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Ingår i: 2013 IEEE Congress on Evolutionary Computation (CEC). - : IEEE. - 9781479904532 ; , s. 1794-1801
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Konferensbidrag (refereegranskat)abstract
- In conformal prediction, predictive models output sets of predictions with a bound on the error rate. In classification, this translates to that the probability of excluding the correct class is lower than a predefined significance level, in the long run. Since the error rate is guaranteed, the most important criterion for conformal predictors is efficiency. Efficient conformal predictors minimize the number of elements in the output prediction sets, thus producing more informative predictions. This paper presents one of the first comprehensive studies where evolutionary algorithms are used to build conformal predictors. More specifically, decision trees evolved using genetic programming are evaluated as conformal predictors. In the experiments, the evolved trees are compared to decision trees induced using standard machine learning techniques on 33 publicly available benchmark data sets, with regard to predictive performance and efficiency. The results show that the evolved trees are generally more accurate, and the corresponding conformal predictors more efficient, than their induced counterparts. One important result is that the probability estimates of decision trees when used as conformal predictors should be smoothed, here using the Laplace correction. Finally, using the more discriminating Brier score instead of accuracy as the optimization criterion produced the most efficient conformal predictions.
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