Research-Backed Strategies to Improve Anchor Recall in Spam Detection

Thorough Explanation: The article discusses various techniques aimed at accelerating the global aggregation of the Anchor algorithm, which is used to compute a set of top-k words with the highest global impact according to different aggregation functions. The proposed techniques include lossless and lossy methods that reduce the computational cost of the algorithm without compromising its accuracy. These techniques can accelerate the computation by up to 30 times, making it possible to perform the analysis in minutes instead of hours. Moreover, the authors propose a probabilistic model that accounts for noise in the Anchor algorithm and diminishes the bias towards frequent words that have low impact. This model helps identify important words that are often overlooked by the algorithm. The article also explores the use of local explanation methods to highlight the input tokens that have a significant impact on the outcome of classifying a document. However, standard aggregation methods are computationally expensive and infeasible for simple users running within a short analysis session. To address this issue, the authors propose techniques for accelerating the global aggregation of the Anchor algorithm. Overall, these techniques can help users gain valuable insights into how a machine learning model works and identify important words that contribute to its accuracy.