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Swarup Chandra

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AAAI Conference 2019 Conference Paper

Multistream Classification with Relative Density Ratio Estimation

Bo Dong
Yang Gao
Swarup Chandra
Latifur Khan

In supervised learning, availability of sufficient labeled data is of prime importance. Unfortunately, they are sparingly available in many real-world applications. Particularly when performing classification over a non-stationary data stream, unavailability of sufficient labeled data undermines the classifier’s long-term performance by limiting its adaptability to changes in data distribution over time. Recently, studies in such settings have appealed to transfer learning techniques over a data stream while detecting drifts in data distribution over time. Here, the data stream is represented by two independent non-stationary streams, one containing labeled data instances (called source stream) having a biased distribution compared to the unlabeled data instances (called target stream). The task of label prediction under this representation is called Multistream Classification, where instances in the two streams occur independently. While these studies have addressed various challenges in the multistream setting, it still suffers from large computational overhead mainly due to frequent bias correction and drift adaptation methods employed. In this paper, we focus on utilizing an alternative bias correction technique, called relative density-ratio estimation, which is known to be computationally faster. Importantly, we propose a novel mechanism to automatically learn an appropriate mixture of relative density that adapts to changes in the multistream setting over time. We theoretically study its properties and empirically demonstrate its superior performance, within a multistream framework called MSCRDR, on benchmark datasets by comparing with other competing methods.

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AAAI Conference 2018 Conference Paper

A Framework for Multistream Regression With Direct Density Ratio Estimation

Ahsanul Haque
Hemeng Tao
Swarup Chandra
Jie Liu
Latifur Khan

Regression over a stream of data is challenging due to unbounded data size and non-stationary distribution over time. Typically, a traditional supervised regression model over a data stream is trained on data instances occurring within a short time period by assuming a stationary distribution. This model is later used to predict value of response-variable in future instances. Over time, the model may degrade in performance due to changes in data distribution among incoming data instances. Updating the model for change adaptation requires true value for every recent data instances, which is scarce in practice. To overcome this issue, recent studies have employed techniques that sample fewer instances to be used for model re-training. Yet, this may introduce sampling bias that adversely affects the model performance. In this paper, we study the regression problem over data streams in a novel setting. We consider two independent, yet related, nonstationary data streams, which are referred to as the source and the target stream. The target stream continuously generates data instances whose value of response variable is unknown. The source stream, however, continuously generates data instances along with corresponding value for the response-variable, and has a biased data distribution with respect to the target stream. We refer to the problem of using a model trained on the biased source stream to predict the response-variable’s value in data instances occurring on the target stream as Multistream Regression. In this paper, we describe a framework for multistream regression that simultaneously overcomes distribution bias and detects change in data distribution represented by the two streams over time using a Gaussian kernel model. We analyze the theoretical properties of the proposed approach and empirically evaluate it on both real-world and synthetic data sets. Importantly, our results indicate superior performance by the framework compared to other baseline regression methods.

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