Deterministic solutions are becoming more critical for interpretability.
Weighted Least-Squares (WLS) has been widely used as a deterministic batch
solution with a specific weight design. In the online settings of WLS, exact
reweighting is necessary to converge to its batch settings. In order to comply
with its necessity, the iteratively reweighted least-squares algorithm is
mainly utilized with a linearly growing time complexity which is not attractive
for online learning. Due to the high and growing computational costs, an
efficient online formulation of reweighted least-squares is desired. We
introduce a new deterministic online classification algorithm of WLS with a
constant time complexity for binary class rebalancing. We demonstrate that our
proposed online formulation exactly converges to its batch formulation and
outperforms existing state-of-the-art stochastic online binary classification
algorithms in real-world data sets empirically