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Classification of examples by multiple agents with private features

机译：具有私有功能的多个代理对示例的分类

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We consider classification tasks where relevant features are distributed among a set of agents and cannot be centralized, for example due to privacy restrictions. We are motivated by a key classification task that arises in a calendar management domain where software assistants classify new meetings as likely to be difficult to schedule. Accurate prediction of the output class is difficult for an isolated single agent because the target concept may involve features to which the agent does not have access, for example each attendee's willingness to attend the meeting. To increase prediction accuracy, novel learning algorithms are required in which agents collaborate to classify new examples while maintaining the privacy of features. We introduce a novel distributed asynchronous decision-tree inspired algorithm for such tasks named DDT. DDT differs from previous approaches in that it applies to vertically partitioned data with categorical multi-valued features, it requires no explicit hypothesis generation, and there is no a priori restriction on number of agents. We present empirical results in our meeting scheduling domain and show that DDT outperforms a single agent learner and performs as well as a centralized learner with hypothetical access to all the features.
机译：我们考虑分类任务，其中相关功能分布在一组代理之间，并且例如由于隐私限制而无法集中化。我们受到日历管理领域中出现的一项关键分类任务的激励，在该日历领域中，软件助手将新会议归类为可能难以安排的会议。对于孤立的单个业务代表，很难准确预测输出类别，因为目标概念可能涉及业务代表无法访问的功能，例如，每个与会者参加会议的意愿。为了提高预测准确性，需要新颖的学习算法，在这种算法中，代理可以协作以对新示例进行分类，同时保持功能的隐私性。我们针对此类任务引入了一种新颖的分布式异步决策树启发式算法，称为DDT。 DDT与以前的方法的不同之处在于，它适用于具有分类多值特征的垂直分区数据，不需要显式的假设生成，并且对代理数量没有先验限制。我们在会议日程安排领域中提供了经验性结果，并表明DDT优于单个代理学习器，并且在假设访问所有功能的情况下，表现也好于集中学习器。

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