Improving Boosting Methods With A Stable Loss Function Handling Outliers

Abstract

In classification problems, the occurrence of abnormal observations is often encountered. How to obtain a stable model to deal with outliers has always been a subject of widespread concern. In this article, we draw on the ideas of the AdaBoosting algorithm and propose a asymptotically linear loss function, which makes the output function more stable for contaminated samples, and two boosting algorithms were designed, based on two different way of updating, to handle outliers. In addition, a skill for overcoming the instability of Newton's method when dealing with weak convexity is introduced. Several samples, where outliers were artificially added, show that the Discrete L-AdaBoost and Real L-AdaBoost Algorithms find the boundary of each category consistently under the condition where data is contaminated. In real world data sets, we show the effectiveness of suggested algorithms by comparison with other two ensemble learning methods, especially for large dataset.

Analysis of the Current Situation, Problems and Countermeasures of China's Petrochemical Industry

“…In order to effectively solve these problems, in recent years China has set strict management requirements for the production and use of chemical products as well as the discharge of pollutants. However, in general, China has not yet carried out systematic investigation of chemical substances in production and use, environmental risk assessment and control, and the relevant legislation and systems have not yet been perfected, which leads to enterprises lack the motivation to improve [6]. In order to promote sustainable development, countries around the world are accelerating the promotion of new energy sources, which will undoubtedly have an impact on the petrochemical industry and prompt its green transformation.…”