arXiv subjects
Publications and source records attributed to Igor E. Kuralenok.
In this paper, we introduce a new approach to multiclass classification problem. We decompose the problem into a series of regression tasks, that are solved with CART trees. The proposed method works significantly faster than state-of-the-art solutions while giving the same level of model quality. The algorithm is also robust to imbalanced datasets, allowing to reach high-quality results in significantly less time without class re-balancing.
Consistency requirements for state-of-the-art stream processing systems are defined in terms of delivery guarantees. Exactly-once is the strongest one and the most desirable for end-user. However, there are several issues regarding this concept. Commonly used techniques that enforce exactly-once produce significant performance overhead. Besides, the notion of exactly-once is not formally defined and does not capture all properties that provide stream processing systems supporting this guarantee. In this paper, we introduce a formal framework that allows us to define streaming guarantees more regularly. We demonstrate that the properties of delivery, consistency, and determinism are tightly connected within distributed stream processing. We also show that having lightweight determinism, it is possible to provide exactly-once with almost no performance overhead. Experiments show that the proposed approach can significantly outperform alternative industrial solutions.