47,802 research outputs found
Robust and cost-effective approach for discovering action rules
The main goal of Knowledge Discovery in
Databases is to find interesting and usable patterns, meaningful
in their domain. Actionable Knowledge Discovery came to
existence as a direct respond to the need of finding more usable
patterns called actionable patterns. Traditional data mining
and algorithms are often confined to deliver frequent patterns
and come short for suggesting how to make these patterns
actionable. In this scenario the users are expected to act.
However, the users are not advised about what to do with
delivered patterns in order to make them usable. In this paper,
we present an automated approach to focus on not only creating
rules but also making the discovered rules actionable.
Up to now few works have been reported in this field which
lacking incomprehensibility to the user, overlooking the cost
and not providing rule generality. Here we attempt to present a
method to resolving these issues. In this paper CEARDM
method is proposed to discover cost-effective action rules from
data. These rules offer some cost-effective changes to
transferring low profitable instances to higher profitable ones.
We also propose an idea for improving in CEARDM method
Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
Multitask Learning is a learning paradigm that deals with multiple different
tasks in parallel and transfers knowledge among them. XOF, a Learning
Classifier System using tree-based programs to encode building blocks
(meta-features), constructs and collects features with rich discriminative
information for classification tasks in an observed list. This paper seeks to
facilitate the automation of feature transferring in between tasks by utilising
the observed list. We hypothesise that the best discriminative features of a
classification task carry its characteristics. Therefore, the relatedness
between any two tasks can be estimated by comparing their most appropriate
patterns. We propose a multiple-XOF system, called mXOF, that can dynamically
adapt feature transfer among XOFs. This system utilises the observed list to
estimate the task relatedness. This method enables the automation of
transferring features. In terms of knowledge discovery, the resemblance
estimation provides insightful relations among multiple data. We experimented
mXOF on various scenarios, e.g. representative Hierarchical Boolean problems,
classification of distinct classes in the UCI Zoo dataset, and unrelated tasks,
to validate its abilities of automatic knowledge-transfer and estimating task
relatedness. Results show that mXOF can estimate the relatedness reasonably
between multiple tasks to aid the learning performance with the dynamic feature
transferring.Comment: accepted by The Genetic and Evolutionary Computation Conference
(GECCO 2020
Inductive queries for a drug designing robot scientist
It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments
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