17,972 research outputs found
Modelling Dependency Structures Produced by the Introduction of a Flipped Classroom
Teaching processes have been changing in the lasts few decades from a traditional lecture-example-homework format to more active strategies to engage the students in the learning process. One of the most popular methodologies is the flipped classroom, where traditional structure of the course is turned over by moving out of the classroom, most basic knowledge acquisition. However, due to the workload involved in this kind of methodology, an objective analysis of the results should be carried out to assess whether the lecturer’s workload is worth the effort or not. In this paper, we compare the results obtained from two different methodologies: traditional lecturing and flipped classroom methodology, in terms of some performance indicators and an attitudinal survey, in an introductory statistics course for engineering students. Finally, we analysed the changes in the relationships among variables of interest when the traditional teaching was moved to a flipped classroom by using Bayesian networks
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
We present a novel hybrid algorithm for Bayesian network structure learning,
called H2PC. It first reconstructs the skeleton of a Bayesian network and then
performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
The algorithm is based on divide-and-conquer constraint-based subroutines to
learn the local structure around a target variable. We conduct two series of
experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is
currently the most powerful state-of-the-art algorithm for Bayesian network
structure learning. First, we use eight well-known Bayesian network benchmarks
with various data sizes to assess the quality of the learned structure returned
by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in
terms of goodness of fit to new data and quality of the network structure with
respect to the true dependence structure of the data. Second, we investigate
H2PC's ability to solve the multi-label learning problem. We provide
theoretical results to characterize and identify graphically the so-called
minimal label powersets that appear as irreducible factors in the joint
distribution under the faithfulness condition. The multi-label learning problem
is then decomposed into a series of multi-class classification problems, where
each multi-class variable encodes a label powerset. H2PC is shown to compare
favorably to MMHC in terms of global classification accuracy over ten
multi-label data sets covering different application domains. Overall, our
experiments support the conclusions that local structural learning with H2PC in
the form of local neighborhood induction is a theoretically well-motivated and
empirically effective learning framework that is well suited to multi-label
learning. The source code (in R) of H2PC as well as all data sets used for the
empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author
Protein (Multi-)Location Prediction: Using Location Inter-Dependencies in a Probabilistic Framework
Knowing the location of a protein within the cell is important for
understanding its function, role in biological processes, and potential use as
a drug target. Much progress has been made in developing computational methods
that predict single locations for proteins, assuming that proteins localize to
a single location. However, it has been shown that proteins localize to
multiple locations. While a few recent systems have attempted to predict
multiple locations of proteins, they typically treat locations as independent
or capture inter-dependencies by treating each locations-combination present in
the training set as an individual location-class. We present a new method and a
preliminary system we have developed that directly incorporates
inter-dependencies among locations into the multiple-location-prediction
process, using a collection of Bayesian network classifiers. We evaluate our
system on a dataset of single- and multi-localized proteins. Our results,
obtained by incorporating inter-dependencies are significantly higher than
those obtained by classifiers that do not use inter-dependencies. The
performance of our system on multi-localized proteins is comparable to a top
performing system (YLoc+), without restricting predictions to be based only on
location-combinations present in the training set.Comment: Peer-reviewed and presented as part of the 13th Workshop on
Algorithms in Bioinformatics (WABI2013
ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects
This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain
BAYESIAN APPROACH TO THE PROCESS OF IDENTIFICATION OF THE DETERMINANTS OF INNOVATIVENESS
Bayesian belief networks are applied in determining the most important factors of the innovativeness level of national economies. The paper is divided into two parts. The first presentsthe basic theory of Bayesian networks whereas in the second, the belief networks have been generated by an inhouse developed computer system called BeliefSEEKER which was implemented to generate the determinants influencing the innovativeness level of national economies.Qualitative analysis of the generated belief networks provided a way to define a set of the most important dimensions influencing the innovativeness level of economies and then the indicators that form these dimensions. It has been proven that Bayesian networks are very effective methods for multidimensional analysis and forming conclusions and recommendations regarding the strength of each innovative determinant influencing the overall performance of a country’s economy
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