Multi-label classification via incremental clustering on an evolving data stream

Abstract

With the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make predictions, contributing to the competitive advantages of companies. The increasing data volume, however, has posed many problems to classical batch learning systems, such as the need to retrain the model completely with the newly arrived samples or the impracticality of storing and accessing a large volume of data. This has prompted interest on incremental learning that operates on data streams. In this study, we develop an incremental online multi-label classification (OMLC) method based on a weighted clustering model. The model is made to adapt to the change of data via the decay mechanism in which each sample's weight dwindles away over time. The clustering model therefore always focuses more on newly arrived samples. In the classification process, only clusters whose weights are greater than a threshold (called mature clusters) are employed to assign labels for the samples. In our method, not only is the clustering model incrementally maintained with the revealed ground truth labels of the arrived samples, the number of predicted labels in a sample are also adjusted based on the Hoeffding inequality and the label cardinality. The experimental results show that our method is competitive compared to several well-known benchmark algorithms on six performance measures in both the stationary and the concept drift settings.

Advancing on weighted PLS-SEM in examining the trust-based recommendation system in pioneering product promotion effectiveness

“…Additionally, Amazon, the world's largest online retailer, also collected users’ data through AI and adopted collaborative recommendation systems to provide suggestions in the light of matching users to similar customers. In fact, Nguyen et al ( 2019 ) mentioned that 35% of Amazon’s revenue came from its recommendation agent, and there is a 29% sales increase since it adopted the recommendation system. These facts support the claim of the importance of recommendation systems in product promotion.…”

Heterogeneous ensemble selection for evolving data streams

“…Classifiers for data streams, therefore, should have the ability to detect and adapt to concept drifts in order to achieve high predictive performance. The evolving data stream setting is the main motivation for the development of numerous online learning algorithms for multi-class classification [9][10][11][12] and multi-label classification [13][14][15]. In this study, we consider the data stream methods for multi-class classification.…”

“…When the true label of the instance 𝒙𝒙 (𝑡𝑡+1) is available, we know the value of 𝑙𝑙 𝑡𝑡+1 , and we can also compute the value of 𝑍𝑍(𝑡𝑡 + 1) through 𝑍𝑍(𝑡𝑡); thus, we can now have access to the value 𝜌𝜌(𝑡𝑡 + 1) by using equation (13).…”

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

“…For training our models, we adopt a batch learning approach that requires retraining the model to incorporate new data from the data stream. Future studies can explore an incremental learning approach (T. T. Nguyen et al., 2019; Read, Bifet, Holmes, & Pfahringer, 2012) to dynamically train models on newly available data from the ongoing/future disasters (NOAA National Centers for Environmental Information (NCEI) U.S., 2018). Such an incremental learning approach is likely to increase the accuracy of the model as it utilizes data from an ongoing disaster.…”