{"repo":"andremun/InstanceSpace","free":true,"listed":false,"github":"https://github.com/andremun/InstanceSpace","clone":"git clone https://github.com/andremun/InstanceSpace.git","description":"MATLAB toolkit for Instance Space Analysis: an automated PRELIM-SIFTED-PILOT-CLOISTER-PYTHIA-TRACE pipeline that projects problem instances into a 2D/3D space and builds algorithm footprints to assess and compare algorithmic power. Powers the MATILDA web platform.","language":"MATLAB","stars":44,"topics":["algorithm-portfolio","algorithm-selection","bayesian-optimization","benchmarking","classification","data-visualization","dimensionality-reduction","feature-selection","genetic-algorithm","instance-space-analysis"],"license":null,"category":"analytics","readme_excerpt":"Instance Space Analysis: A toolkit for the assessment of algorithmic power Instance Space Analysis is a methodology for assessing the strengths and weaknesses of an algorithm and objectively comparing its algorithmic power, without bias introduced by a restricted choice of test instances. At its core is the modelling of the relationship between an instance's structural properties and the performance of a group of algorithms. Instance Space Analysis allows the construction of footprints for each algorithm, defined as regions in the instance space where we statistically infer good performance. Other insights that can be gathered from Instance Space Analysis include: - Objective metrics of each algorithm’s footprint across the instance space as a measure of algorithmic power; - Explanation through visualisation of how instance features correlate with algorithm performance in various regions of the instance space; - Visualisation of the distribution and diversity of existing benchmark and real-world instances; - Assessment of the adequacy of the features used to characterise an instance; - Partitioning of the instance space into recommended regions for automated algorithm selection; - Distinguishing areas of the instance space where it may be useful to generate additional instances to gain further insights. The unique advantage of visualising algorithm performance in the instance space, rather than as a small set of summary statistics averaged across a selected collection of inst","default_branch":null,"files":null,"tree":[],"storefront":"/r/andremun","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/andremun/InstanceSpace/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}