3,024 research outputs found

    Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales

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    Predicting the class of a customer profile is a key task in marketing, which enables businesses to approach the right customer with the right product at the right time through the right channel to satisfy the customer's evolving needs. However, due to costs, privacy and/or data protection, only the business' owned transactional data is typically available for constructing customer profiles. Predicting the class of customer profiles based on such data is challenging, as the data tends to be very large, heavily sparse and highly skewed. We present a new approach that is designed to efficiently and accurately handle the multi-class classification of customer profiles built using sparse and skewed transactional data. Our approach first bins the customer profiles on the basis of the number of items transacted. The discovered bins are then partitioned and prototypes within each of the discovered bins selected to build the multi-class classifier models. The results obtained from using four multi-class classifiers on real-world transactional data from the food sales domain consistently show the critical numbers of items at which the predictive performance of customer profiles can be substantially improved

    Adversarial Unsupervised Representation Learning for Activity Time-Series

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    Sufficient physical activity and restful sleep play a major role in the prevention and cure of many chronic conditions. Being able to proactively screen and monitor such chronic conditions would be a big step forward for overall health. The rapid increase in the popularity of wearable devices provides a significant new source, making it possible to track the user's lifestyle real-time. In this paper, we propose a novel unsupervised representation learning technique called activity2vec that learns and "summarizes" the discrete-valued activity time-series. It learns the representations with three components: (i) the co-occurrence and magnitude of the activity levels in a time-segment, (ii) neighboring context of the time-segment, and (iii) promoting subject-invariance with adversarial training. We evaluate our method on four disorder prediction tasks using linear classifiers. Empirical evaluation demonstrates that our proposed method scales and performs better than many strong baselines. The adversarial regime helps improve the generalizability of our representations by promoting subject invariant features. We also show that using the representations at the level of a day works the best since human activity is structured in terms of daily routinesComment: Accepted at AAAI'19. arXiv admin note: text overlap with arXiv:1712.0952

    Proceedings of the 2nd Computer Science Student Workshop: Microsoft Istanbul, Turkey, April 9, 2011

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    A Comprehensive Survey on Rare Event Prediction

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    Rare event prediction involves identifying and forecasting events with a low probability using machine learning and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the machine learning pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistical and machine learning. This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches. Specifically, we consider 73 datasets from different modalities (i.e., numerical, image, text, and audio), four major categories of data processing, five major algorithmic groupings, and two broader evaluation approaches. This paper aims to identify gaps in the current literature and highlight the challenges of predicting rare events. It also suggests potential research directions, which can help guide practitioners and researchers.Comment: 44 page

    Comparative study of state-of-the-art machine learning models for analytics-driven embedded systems

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    Analytics-driven embedded systems are gaining foothold faster than ever in the current digital era. The innovation of Internet of Things(IoT) has generated an entire ecosystem of devices, communicating and exchanging data automatically in an interconnected global network. The ability to efficiently process and utilize the enormous amount of data being generated from an ensemble of embedded devices like RFID tags, sensors etc., enables engineers to build smart real-world systems. Analytics-driven embedded system explores and processes the data in-situ or remotely to identify a pattern in the behavior of the system and in turn can be used to automate actions and embark decision making capability to a device. Designing an intelligent data processing model is paramount for reaping the benefits of data analytics, because a poorly designed analytics infrastructure would degrade the systemā€™s performance and effectiveness. There are many different aspects of this data that make it a more complex and challenging analytics task and hence a suitable candidate for big data. Big data is mainly characterized by its high volume, hugely varied data types and high speed of data receipt; all these properties mandate the choice of correct data mining techniques to be used for designing the analytics model. Datasets with images like face recognition, satellite images would perform better with deep learning algorithms, time-series datasets like sensor data from wearable devices would give better results with clustering and supervised learning models. A regression model would suit best for a multivariate dataset like appliances energy prediction data, forest fire data etc. Each machine learning task has a varied range of algorithms which can be used in combination to create an intelligent data analysis model. In this study, a comprehensive comparative analysis was conducted using different datasets freely available on online machine learning repository, to analyze the performance of state-of-art machine learning algorithms. WEKA data mining toolkit was used to evaluate C4.5, NaĆÆve Bayes, Random Forest, kNN, SVM and Multilayer Perceptron for classification models. Linear regression, Gradient Boosting Machine(GBM), Multilayer Perceptron, kNN, Random Forest and Support Vector Machines (SVM) were applied to dataset fit for regression machine learning. Datasets were trained and analyzed in different experimental setups and a qualitative comparative analysis was performed with k-fold Cross Validation(CV) and paired t-test in Weka experimenter
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