activity
20112024
most citedCASP-DM: Context Aware Standard Process for Data Mining

22 citations · 45 across the 8 of their papers we have counts for

collaborators

8 papers

cs.AI2024★ 6 cited

Learning Alternative Ways of Performing a Task

David Nieves, María José Ramírez-Quintana, Carlos Monserrat +2

A common way of learning to perform a task is to observe how it is carried out by experts. However, it is well known that for most tasks there is no unique way to perform them. Thi…

cs.LG2019★ 4 cited

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…

cs.AI2018★ 3 cited

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…

cs.DB2017★ 22 cited

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…

cs.AI2015★ 1 cited

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…

cs.LG2013★ 4 cited

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…