A Fast Graph Search Algorithm with Dynamic Optimization and Reduced Histogram for Discrimination of Binary Classification Problem
This work addresses binary classification problems, such as fitness assessment, by providing an incremental improvement to SVM models with faster and more accurate discrimination.
The study tackled binary classification by developing a graph search algorithm with dynamic optimization and a reduced histogram method to improve discrimination paths, achieving a 90% reduction in false positives with only a 5% loss in true positives and generating 39 ranked paths in 9 seconds on a dataset of 328,464 objects.
This study develops a graph search algorithm to find the optimal discrimination path for the binary classification problem. The objective function is defined as the difference of variations between the true positive (TP) and false positive (FP). It uses the depth first search (DFS) algorithm to find the top-down paths for discrimination. It proposes a dynamic optimization procedure to optimize TP at the upper levels and then reduce FP at the lower levels. To accelerate computing speed with improving accuracy, it proposes a reduced histogram algorithm with variable bin size instead of looping over all data points, to find the feature threshold of discrimination. The algorithm is applied on top of a Support Vector Machine (SVM) model for a binary classification problem on whether a person is fit or unfit. It significantly improves TP and reduces FP of the SVM results (e.g., reduced FP by 90% with a loss of only\ 5% TP). The graph search auto-generates 39 ranked discrimination paths within 9 seconds on an input of total 328,464 objects, using a dual-core Laptop computer with a processor of 2.59 GHz.