Data-driven satisficing measure and ranking

Data-driven satisficing measure and ranking

We propose an computational framework for real-time risk assessment and prioritizing for random outcomes without prior information on probability distributions. The basic model is built based on satisficing measure (SM) which yields a single index for risk comparison. Since SM is a dual representation for a family of risk measures, we consider problems constrained by Conditional value-at-risk (...

Data-Driven Relevance Judgments for Ranking Evaluation

Ranking evaluation metrics are a fundamental element of design and improvement efforts in information retrieval. We observe that most popular metrics disregard information portrayed in the scores used to derive rankings, when available. This may pose a numerical scaling problem, causing an underor over-estimation of the evaluation depending on the degree of divergence between the scores of rank...

Ranking of generalized fuzzy numbers using distance measure and similarity measure

Context as Relevance-Driven Abduction and Charitable Satisficing

It has been widely assumed that the full meaning of a linguistic expression can be grasped only within a situation, the context of the utterance. There is even agreement that certain factors within the situation are particularly significant, including gestures and facial expressions of the participants, their social roles, the setting of the exchange, the objects surrounding the participants, t...

The Feature Importance Ranking Measure

Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly accessible to humans and cannot easily be used to gain insights about the application domain. Therefore, one often resorts to linear models in combination with variable selection, thereby sacrificing some predictive power f...