32,428 research outputs found
Low Size-Complexity Inductive Logic Programming: The East-West Challenge Considered as a Problem in Cost-Sensitive Classification
The Inductive Logic Programming community has considered
proof-complexity and model-complexity, but, until recently,
size-complexity has received little attention. Recently a
challenge was issued "to the international computing community"
to discover low size-complexity Prolog programs for classifying
trains. The challenge was based on a problem first proposed by
Ryszard Michalski, 20 years ago. We interpreted the challenge
as a problem in cost-sensitive classification and we applied a
recently developed cost-sensitive classifier to the competition.
Our algorithm was relatively successful (we won a prize). This
paper presents our algorithm and analyzes the results of the
competition
A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction
This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalized rule induction. The framework emphasizes the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization
Inference in classifier systems
Classifier systems (Css) provide a rich framework for learning and induction, and they have beenı successfully applied in the artificial intelligence literature for some time. In this paper, both theı architecture and the inferential mechanisms in general CSs are reviewed, and a number of limitations and extensions of the basic approach are summarized. A system based on the CS approach that is capable of quantitative data analysis is outlined and some of its peculiarities discussed
Local Rule-Based Explanations of Black Box Decision Systems
The recent years have witnessed the rise of accurate but obscure decision
systems which hide the logic of their internal decision processes to the users.
The lack of explanations for the decisions of black box systems is a key
ethical issue, and a limitation to the adoption of machine learning components
in socially sensitive and safety-critical contexts. %Therefore, we need
explanations that reveals the reasons why a predictor takes a certain decision.
In this paper we focus on the problem of black box outcome explanation, i.e.,
explaining the reasons of the decision taken on a specific instance. We propose
LORE, an agnostic method able to provide interpretable and faithful
explanations. LORE first leans a local interpretable predictor on a synthetic
neighborhood generated by a genetic algorithm. Then it derives from the logic
of the local interpretable predictor a meaningful explanation consisting of: a
decision rule, which explains the reasons of the decision; and a set of
counterfactual rules, suggesting the changes in the instance's features that
lead to a different outcome. Wide experiments show that LORE outperforms
existing methods and baselines both in the quality of explanations and in the
accuracy in mimicking the black box
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