25,862 research outputs found
On Cognitive Preferences and the Plausibility of Rule-based Models
It is conventional wisdom in machine learning and data mining that logical
models such as rule sets are more interpretable than other models, and that
among such rule-based models, simpler models are more interpretable than more
complex ones. In this position paper, we question this latter assumption by
focusing on one particular aspect of interpretability, namely the plausibility
of models. Roughly speaking, we equate the plausibility of a model with the
likeliness that a user accepts it as an explanation for a prediction. In
particular, we argue that, all other things being equal, longer explanations
may be more convincing than shorter ones, and that the predominant bias for
shorter models, which is typically necessary for learning powerful
discriminative models, may not be suitable when it comes to user acceptance of
the learned models. To that end, we first recapitulate evidence for and against
this postulate, and then report the results of an evaluation in a
crowd-sourcing study based on about 3.000 judgments. The results do not reveal
a strong preference for simple rules, whereas we can observe a weak preference
for longer rules in some domains. We then relate these results to well-known
cognitive biases such as the conjunction fallacy, the representative heuristic,
or the recogition heuristic, and investigate their relation to rule length and
plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus
on plausibility and relation to interpretability, comprehensibility, and
justifiabilit
Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families
The Numeric Taxonomy aims to group operational taxonomic units in clusters (OTUs or taxons or taxa), using the denominated structure analysis by means of numeric methods. These clusters that constitute families are the purpose of this series of projects and they emerge of the structural analysis, of their phenotypical characteristic, exhibiting the relationships in terms of grades of similarity of the OTUs, employing tools such as i) the Euclidean distance and ii) nearest neighbor techniques. Thus taxonomic evidence is gathered so as to quantify the similarity for each pair of OTUs (pair-group method) obtained from the basic data matrix and in this way the significant concept of spectrum of the OTUs is introduced, being based the same one on the state of their characters. A new taxonomic criterion is thereby formulated and a new approach to Computational Taxonomy is presented, that has been already employed with reference to Data Mining, when apply of Machine Learning techniques, in particular to the C4.5 algorithms, created by Quinlan, the degree of efficiency achieved by the TDIDT familyÂŽs algorithms when are generating valid models of the data in classification problems with the Gain of Entropy through Maximum Entropy Principle.Fil: Perichinsky, Gregorio. Universidad de Buenos Aires. Facultad de IngenierĂa; ArgentinaFil: JimĂ©nez Rey, Elizabeth Miriam. Universidad de Buenos Aires. Facultad de IngenierĂa; ArgentinaFil: Grossi, MarĂa Delia. Universidad de Buenos Aires. Facultad de IngenierĂa; ArgentinaFil: Vallejos, FĂ©lix Anibal. Universidad de Buenos Aires. Facultad de IngenierĂa; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias AstronĂłmicas y GeofĂsicas; ArgentinaFil: Servetto, Arturo Carlos. Universidad de Buenos Aires. Facultad de IngenierĂa; ArgentinaFil: Orellana, Rosa Beatriz. Universidad Nacional de La Plata. Facultad de Ciencias AstronĂłmicas y GeofĂsicas; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; ArgentinaFil: Plastino, Ăngel Luis. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de FĂsica; Argentin
Feature construction using explanations of individual predictions
Feature construction can contribute to comprehensibility and performance of
machine learning models. Unfortunately, it usually requires exhaustive search
in the attribute space or time-consuming human involvement to generate
meaningful features. We propose a novel heuristic approach for reducing the
search space based on aggregation of instance-based explanations of predictive
models. The proposed Explainable Feature Construction (EFC) methodology
identifies groups of co-occurring attributes exposed by popular explanation
methods, such as IME and SHAP. We empirically show that reducing the search to
these groups significantly reduces the time of feature construction using
logical, relational, Cartesian, numerical, and threshold num-of-N and X-of-N
constructive operators. An analysis on 10 transparent synthetic datasets shows
that EFC effectively identifies informative groups of attributes and constructs
relevant features. Using 30 real-world classification datasets, we show
significant improvements in classification accuracy for several classifiers and
demonstrate the feasibility of the proposed feature construction even for large
datasets. Finally, EFC generated interpretable features on a real-world problem
from the financial industry, which were confirmed by a domain expert.Comment: 54 pages, 10 figures, 22 table
Elaboration in Dependent Type Theory
To be usable in practice, interactive theorem provers need to provide
convenient and efficient means of writing expressions, definitions, and proofs.
This involves inferring information that is often left implicit in an ordinary
mathematical text, and resolving ambiguities in mathematical expressions. We
refer to the process of passing from a quasi-formal and partially-specified
expression to a completely precise formal one as elaboration. We describe an
elaboration algorithm for dependent type theory that has been implemented in
the Lean theorem prover. Lean's elaborator supports higher-order unification,
type class inference, ad hoc overloading, insertion of coercions, the use of
tactics, and the computational reduction of terms. The interactions between
these components are subtle and complex, and the elaboration algorithm has been
carefully designed to balance efficiency and usability. We describe the central
design goals, and the means by which they are achieved
A Systematic Approach to Constructing Families of Incremental Topology Control Algorithms Using Graph Transformation
In the communication systems domain, constructing and maintaining network
topologies via topology control (TC) algorithms is an important cross-cutting
research area. Network topologies are usually modeled using attributed graphs
whose nodes and edges represent the network nodes and their interconnecting
links. A key requirement of TC algorithms is to fulfill certain consistency and
optimization properties to ensure a high quality of service. Still, few
attempts have been made to constructively integrate these properties into the
development process of TC algorithms. Furthermore, even though many TC
algorithms share substantial parts (such as structural patterns or tie-breaking
strategies), few works constructively leverage these commonalities and
differences of TC algorithms systematically. In previous work, we addressed the
constructive integration of consistency properties into the development
process. We outlined a constructive, model-driven methodology for designing
individual TC algorithms. Valid and high-quality topologies are characterized
using declarative graph constraints; TC algorithms are specified using
programmed graph transformation. We applied a well-known static analysis
technique to refine a given TC algorithm in a way that the resulting algorithm
preserves the specified graph constraints.
In this paper, we extend our constructive methodology by generalizing it to
support the specification of families of TC algorithms. To show the feasibility
of our approach, we reneging six existing TC algorithms and develop e-kTC, a
novel energy-efficient variant of the TC algorithm kTC. Finally, we evaluate a
subset of the specified TC algorithms using a new tool integration of the graph
transformation tool eMoflon and the Simonstrator network simulation framework.Comment: Corresponds to the accepted manuscrip
Computational and Mathematical Modelling of the EGF Receptor System
This chapter gives an overview of computational and mathematical modelling of the EGF receptor system. It begins with a survey of motivations for producing such models, then describes the main approaches that are taken to carrying out such modelling, viz. differential equations and individual-based modelling. Finally, a number of projects that applying modelling and simulation techniques to various aspects of the EGF receptor system are described
TeachOregon: Year 2 Evaluation Report
Foundations for a Better Oregon and the Chalkboard Project launched TeachOregon in 2012 to provide the opportunity for university teacher preparation programs and their community college and school district partners to work together to cocreate and pilot innovative models for collaborative, needs driven teacher preparation. The evaluation for the 3 year project is assessing (1) program implementation through a process study and (2) short term program outcomes through an impact study. The first two years of the TeachOregon initiative have featured important shifts in practice and an ongoing focus on a road set of interventions. The highlights of the Year 2 process study, based primarily on project teams' focus group descriptions of Year 2 work, can be summarized as follows. TeachOregon grantees continue to be actively engaged and highly committed to implementing the road set of interventions introduced during Year 1. TeachOregon grantees report positive changes are taking place in each of the blueprint areas. Partners are planning for TeachOregon activities to continue beyond the grant.TeachOregon provides a framework and resources for responding to externally driven changes (e.g., edTPA, program accreditation).Partners are successfully navigating leadership changes and internal and external communication challenges.Partners are working to overcome internal and institutional barriers to change.Collecting and reporting data required for the impact evaluation presented significant challenges to the grantees. In some cases, partners had never reviewed and shared components of their data
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