My review of “Worker skill estimation in team-based tasks” paper by Rahman, Thirumuruganathan, Roy, Amer-Yahia and Das, is now available on Computing Reviews here. (Requires ACM membership, and it is incidentally, my 11th review for ACM Computing Reviews – you can find the other ones here.)
Companies generally struggle with the problem of estimating the skills of individuals, as it is a hard thing to keep updated and has enormous applications in optimal team formation. Skills are sometimes subjective, can have many variations (Spoken French expertise, or Written Legal French expertise, for example) and change rapidly over time. Historically, skill management used to be only for large companies implementing million dollar SAP HR system implementations, but with the advent of cloud hosted SaaS applications, that situation is now changing. I am fortunate to work on the BizMerlin‘s exciting skill matrix product, so this paper was all the more interesting for me.
Classification is an active research problem, and numerous classification algorithms have been proposed over the past few years. Some algorithms perform better than others, based on the dataset. “No Silver Bullet” or “No Free Lunch Theorem” is an informal theorem that states that no single classification algorithm outperforms other classification algorithms on all data sets. This informal theorem is essentially what keeps many data scientists in business – each data set has its own idiosyncrasies, and different classification algorithms need to be explored to find the one that best meets the needs of the problem at hand. Continue reading full review.