Abstract. Most existing project assessment relies on expert scoring, whose precision can be deteriorated by personal subjectivity. This paper presents an assessment method to compare the advantages and disadvantages of three cohesion patterns between suburban and urban rail transit network, which reduces the influence of subjective score. A modified rough set -grey correlation model is developed as a core of this assessment, based on the integration of rough set theory and grey correlational analysis, where an index system is set up for model calculation. A case study using the network in Ningbo is applied to demonstrate the effectiveness of the method, the results show that the method is more effective using discretely distributed data sensitive to sample size. The consistency of the results in comparison with marginal cost analysis can be a preliminary verification of the model.
We introduce a new library named abess that implements a unified framework of best-subset selection for solving diverse machine learning problems, e.g., linear regression, classification, and principal component analysis. Particularly, the abess certifiably gets the optimal solution within polynomial times under the linear model. Our efficient implementation allows abess to attain the solution of best-subset selection problems as fast as or even 100x faster than existing competing variable (model) selection toolboxes. Furthermore, it supports common variants like best group subset selection and 2 regularized best-subset selection. The core of the library is programmed in C++. For ease of use, a Python library is designed for conveniently integrating with scikit-learn, and it can be installed from the Python library Index 1 .
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