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Understanding customer behavior is necessary to develop efficient marketing strategies or launch tailored programs with social value for the public. Customer segmentation is a critical task for understanding diverse and dynamic customer behavior. However, as the popularity of different products varies, building dynamic customer behavior models for products with few customers may overfit the data. In this paper, we propose a new Bayesian nonparametric model for dynamic customer segmentation—Hierarchical Fragmentation-Coagulation Processes (HFCP), which allows sharing behavior patterns across multiple products. We conduct comprehensive empirical evaluations using two real-world purchase datasets. Our results show that HFCP can: (i) determine the number of groups required to model diverse customer behavior automatically; (ii) capture the changes such as split and merge of customer groups over time; (iii) discover behavior patterns shared among products and identify products with similar or different purchase behavior impacted by promotion, brand choice and change of seasons; and (iv) overcome overfitting problems and outperform previous customer segmentation models on estimating behavior for unseen customers. Hence, HFCP is a flexible and accurate segmentation model that can be used by stakeholders to understand dynamic customer behavior and compare the purchase behavior for different products.

© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Obesity is a growing problem affecting millions of people. Various behavior change programs have been designed to reduce its prevalence. An Australian supermarket has recently run a web-based health program to motivate people to eat healthily and do more physical activity. The program offered discounts on fresh products and a website, HealthierU, providing interactive support tools for participants. The stakeholders desire to evaluate if the program is effective and if the supporting website is useful to facilitate behavior changes. To answer these questions, in this work we propose a method to: (1) model individual purchase rate from sparse recorded transactions through a mixture of Non-Homogeneous Poisson Processes (NHPP), (2) design criteria for partitioning participants based on their interactions with the HealthierU website, (3) evaluate the program impact by comparing behavior changes across different groups of participants. Our case study shows that during the program the participants significantly increased their purchases of some fresh products. Both the distribution of behavior patterns and impact scores show that the program imposed relatively strong impact on the participants who logged activities and tracked weights. Our method can facilitate the enhancement of personalized health programs, especially aiming to maximize the program impact and targeting participants through web or mobile applications.

© 2017 Elsevier Ltd It is well established that multiple EEG channels are required for various brain functionality studies, including classification tasks. Yet, due to the curse of dimensionality problem, the analysis of multiple channels may not lead to the desired performance. Accordingly, a number of static channel selection algorithms have been proposed to identify the most relevant subset of channels. However, static methods select a fixed subset of channels that is unchanged when processing new data, and hence cannot adapt to changes in data. In this paper, we propose a novel algorithm that utilizes the dynamic classification behaviour of channels in selecting the channel that is most relevant for each time segment of the signal. The main idea is to identify for each time segment of every channel of the signal (testing sample) the closest training samples. These training samples are used to estimate the local accuracy of each channel. The best performing channel for that time segment will then be identified as the relevant one. Results obtained using EEG data of a four-class alertness state classification problem, with two different feature sets, reveal that the proposed approach is capable of achieving competitive performance compared to a traditional static channel selection based method. More importantly, the evaluation of the selected channels reveals that our approach is able to select relevant channels for each of the four alertness states. The proposed algorithm is expected to make a valuable contribution to the field of multichannel biomedical signal classification.

© Springer-Verlag GmbH Germany 2017. Undesirable correlations between sensitive attributes (such as race, gender or personal status) and the class label (such as recruitment decision and approval of credit card), may lead to biased decision in data analytics. In this paper, we investigate how to build discrimination-aware models even when the available training set is intrinsically discriminating based on the sensitive attributes. We propose a new classification method called Discrimination-Aware Association Rule classifier (DAAR), which integrates a new discrimination-aware measure and an association rule mining algorithm. We evaluate the performance of DAAR on three real datasets from different domains and compare DAAR with two non-discrimination-aware classifiers (a standard association rule classification algorithm and the state-of-the-art association rule algorithm SPARCCC), and also with a recently proposed discrimination-aware decision tree method. Our comprehensive evaluation is based on three measures: predictive accuracy, discrimination score and inclusion score. The results show that DAAR is able to effectively filter out the discriminatory rules and decrease the discrimination severity on all datasets with insignificant impact on the predictive accuracy. We also find that DAAR generates a small set of rules that are easy to understand and applied by users, to help them make discrimination-free decisions.

© 2016 IEEE. The multichannel nature of EEG and EMG data poses a big challenge to the development of automatic EEG/EMG analysis and classification systems. Due to the 'curse of dimensionality' problem, the analysis and classification of several channels may not lead to the desired performance. Accordingly, a number of algorithms have been proposed to identify small 'static' subsets of channels that are capable of differentiating between samples of different classes. However, the identification of small subsets of relevant channels may not always be possible, where for certain applications the smaller the number of channels the less chance that sufficient information is provided. We propose in this paper a dynamic channel selection algorithm that identifies a channel (or a subset of channels) for each time segment of the signal that is relevant to the class of that particular time segment. To achieve this, we embraced the dynamic classifier selection methodology, and particularly the multiple classifier behaviour approach. Each EEG/EMG channel can be chosen to represent a certain unseen time segment of the signal based on the performance, or local accuracy, of its nearest neighbours in the set of training time segments. Results obtained using EEG data of a four-class alertness state classification problem reveal that the proposed approach is capable of achieving competitive performance compared to a traditional static channel selection based method. The algorithm also produced very encouraging results when used to classify EMG data collected from nine transradial amputees performing six classes of movements.