Knowledge discovery from data?

By Michael J. Pazzani. 2000

173 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

In Information Systems

Citations in the corpus

Supersparse linear integer models for optimized medical scoring systems (2015, background): “In practice, interpretability is a subjective andmultifaceted notion (i.e., it depends on who will be using the model but also depends on multiple model qualities as discussed by Kodratoff 1994; Pazzani 2000; Freitas 2014).”
Falling Rule Lists (2014, background): “Interpretability is very context dependent (see Kodratoﬀ, 1994; Pazzani, 2000; Freitas, 2014; Huys-mans et al. , 2011; Allahyari & Lavesson, 2011; Martens & Baesens, 2010; R¨uping, 2006; Verbeke et al. , 2011; Martens et al. , 2011), and no matter how one measures it in one domain, it can be…”
Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, neutral): “Streaming learning is a special case of incremental batch learning where the batch size is set to one (Nt = 1) and the agent is only allowed a single epoch through the labelled training dataset (Gama, 2010; Gama et al., 2013).”
Methods and Models for Interpretable Linear Classification (2014, background): “The process of creating models that convey such qualitative understanding, however, is inherently complicated due to the fact that interpretability is a subjective and multifaceted notion [57, 86, 35].”
Causal Falling Rule Lists (2015, background): “Interpretability is highly con- text dependent (Kodratoff 1994; Pazzani 2000; Martens et al. 2011); no matter how one measures it in one domain, it can be different in the next domain.”
Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice (2018, background): “What are the desired characteristics of an interpretable model, if one exists? The answer to this question changes for each audience and application (33, 39, 18).”
Understanding the Crucial Role of Attribute Interaction in Data Mining (2001, background): “clearly relevant for data mining, as pointed out e.g. by Pazzani (2000) .”
Interpretability of machine learning models and representations: an introduction (2016, background): “Although interpretability is often associated with the size of the model, Pazzani wrote in 2000 that "there has been no study that shows that people ﬁnd smaller models more comprehen-sibible or that the size of a model is the only factor that aﬀects its comprehen-sibility" [4].”
User-oriented Assessment of Classification Model Understandability (2011, unknown): “Moreover, a few tangible suggestions have been presented on how to measure the understandability of patterns and on how to make patterns and models more understandable [11].”
Uniqueness of medical data mining (2002, background): “We must take into account the human cognitive processes, learn how people assimilate new knowledge to increase the usefulness of the new generation of better DMKD tools [38] if we are to make progress.”