Nowadays, machine learning classification techniques have been successfully used while building data-driven intelligent predictive systems in various application areas including smartphone apps. For an effective context-aware system, context pre-modeling is considered as a key issue and task, as the representation of contextual data directly influences the predictive models. This paper mainly explores the role of major context pre-modeling tasks, such as context vectorization by defining a good numerical measure through transformation and normalization, context generation and extraction by creating new brand principal components, context selection by taking into account a subset of original contexts according to their correlations, and eventually context evaluation, to build effective context-aware predictive models utilizing multi-dimensional contextual data. For creating models, various popular machine learning classification techniques such as decision tree, random forest, k-nearest neighbor, support vector machines, naive Bayes classifier, and deep learning by constructing a neural network of multiple hidden layers, are used in our study. Based on the context pre-modeling tasks and classification methods, we experimentally analyze user-centric smartphone usage behavioral activities utilizing their contextual datasets. The effectiveness of these machine learning context-aware models is examined by considering prediction accuracy, in terms of precision, recall, f-score, and ROC values, and has been made an empirical discussion in various dimensions within the scope of our study.

中文翻译：

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上下文预建模：基于分类的以用户为中心的上下文感知的预测建模的经验分析

如今，在包括智能手机应用程序在内的各种应用程序领域构建数据驱动的智能预测系统时，已经成功使用了机器学习分类技术。对于有效的上下文感知系统，上下文预建模被视为关键问题和任务，因为上下文数据的表示直接影响预测模型。本文主要探讨了主要的上下文预建模任务的作用，例如通过转换和归一化定义良好的数值度量，通过创建新的品牌主成分创建上下文和提取上下文，选择上下文，进行上下文选择来进行上下文向量化。通过根据原始上下文的相关性考虑它们的子集，并最终进行上下文评估，以利用多维上下文数据建立有效的上下文感知预测模型。为了创建模型，各种流行的机器学习分类在我们的研究中使用了决策树，随机森林，k最近邻，支持向量机，朴素贝叶斯分类器和通过构建多个隐藏层的神经网络进行深度学习等技术。基于上下文预建模任务和分类方法，我们通过使用上下文数据集对用户为中心的智能手机使用行为进行实验性分析。这些机器学习上下文感知模型的有效性通过在精度，召回率，f分数和ROC值方面考虑了预测准确性进行了检验，并且在我们研究的范围内进行了多个维度的实证讨论。