22 citations · 63 across the 7 of their papers we have counts for
7 papers
Fairness and Missing Values
Fernando Martínez-Plumed, Cèsar Ferri, David Nieves +1
The causes underlying unfair decision making are complex, being internalised in different ways by decision makers, other actors dealing with data and models, and ultimately by the…
Analysing Results from AI Benchmarks: Key Indicators and How to Obtain Them
Fernando Martínez-Plumed, José Hernández-Orallo
Item response theory (IRT) can be applied to the analysis of the evaluation of results from AI benchmarks. The two-parameter IRT model provides two indicators (difficulty and discr…
General-purpose Declarative Inductive Programming with Domain-Specific Background Knowledge for Data Wrangling Automation
Lidia Contreras-Ochando, César Ferri, José Hernández-Orallo +3
Given one or two examples, humans are good at understanding how to solve a problem independently of its domain, because they are able to detect what the problem is and to choose th…
A multidisciplinary task-based perspective for evaluating the impact of AI autonomy and generality on the future of work
Enrique Fernández-Macías, Emilia Gómez, José Hernández-Orallo +4
This paper presents a multidisciplinary task approach for assessing the impact of artificial intelligence on the future of work. We provide definitions of a task from two main pers…
CASP-DM: Context Aware Standard Process for Data Mining
Fernando Martínez-Plumed, Lidia Contreras-Ochando, Cèsar Ferri +5
We propose an extension of the Cross Industry Standard Process for Data Mining (CRISPDM) which addresses specific challenges of machine learning and data mining for context and mod…
Forgetting and consolidation for incremental and cumulative knowledge acquisition systems
Fernando Martínez-Plumed, Cèsar Ferri, José Hernández-Orallo +1
The application of cognitive mechanisms to support knowledge acquisition is, from our point of view, crucial for making the resulting models coherent, efficient, credible, easy to…
On the definition of a general learning system with user-defined operators
Fernando Martínez-Plumed, Cèsar Ferri, José Hernández-Orallo +1
In this paper, we push forward the idea of machine learning systems whose operators can be modified and fine-tuned for each problem. This allows us to propose a learning paradigm w…