47 research outputs found
Cost-sensitive Bayesian network learning using sampling
A significant advance in recent years has been the development of cost-sensitive decision tree learners, recognising that real world classification problems need to take account of costs of misclassification and not just focus on accuracy. The literature contains well over 50 cost-sensitive decision tree induction algorithms, each with varying performance profiles. Obtaining good Bayesian networks can be challenging and hence several algorithms have been proposed for learning their structure and parameters from data. However, most of these algorithms focus on learning Bayesian networks that aim to maximise the accuracy of classifications. Hence an obvious question that arises is whether it is possible to develop cost-sensitive Bayesian networks and whether they would perform better than cost-sensitive decision trees for minimising classification cost? This paper explores this question by developing a new Bayesian network learning algorithm based on changing the data distribution to reflect the costs of misclassification. The proposed method is explored by conducting experiments on over 20 data sets. The results show that this approach produces good results in comparison to more complex cost-sensitive decision tree algorithms
An introduction to crowdsourcing for language and multimedia technology research
Language and multimedia technology research often relies on
large manually constructed datasets for training or evaluation of algorithms and systems. Constructing these datasets is often expensive with significant challenges in terms of recruitment of personnel to carry out the work. Crowdsourcing methods using scalable pools of workers available on-demand offers a flexible means of rapid low-cost construction of many of these datasets to support existing research requirements and potentially promote new research initiatives that would otherwise not be possible
Search for the doubly heavy baryon decaying to
A first search for the
decay is performed by the LHCb experiment with a data sample of proton-proton
collisions, corresponding to an integrated luminosity of
recorded at centre-of-mass energies of 7, 8, and . Two peaking structures are seen with a local (global) significance of
and standard deviations at masses of
and , respectively. Upper limits are set on the baryon
production cross-section times the branching fraction relative to that of the
decay at centre-of-mass energies of 8 and
, in the and in the
rapidity and transverse-momentum ranges from 2.0 to 4.5 and 0 to
, respectively. Upper limits are presented
as a function of the mass and lifetime.Comment: All figures and tables, along with machine-readable versions and any
supplementary material and additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-005.html (LHCb
public pages
Measurement of CP asymmetries and branching fraction ratios of Bâ decays to two charm mesons
The asymmetries of seven decays to two charm mesons are measured using data corresponding to an integrated luminosity of of proton-proton collisions collected by the LHCb experiment. Decays involving a or meson are analysed by reconstructing only the or decay products. This paper presents the first measurement of and , and the most precise measurement of the other five asymmetries. There is no evidence of violation in any of the analysed decays. Additionally, two ratios between branching fractions of selected decays are measured.The CP asymmetries of seven B decays to two charm mesons are measured using data corresponding to an integrated luminosity of 9 fb of proton-proton collisions collected by the LHCb experiment. Decays involving a D or meson are analysed by reconstructing only the D or decay products. This paper presents the first measurement of (BâD) and (BâD), and the most precise measurement of the other five CP asymmetries. There is no evidence of CP violation in any of the analysed decays. Additionally, two ratios between branching fractions of selected decays are measured.[graphic not available: see fulltext]The asymmetries of seven decays to two charm mesons are measured using data corresponding to an integrated luminosity of of proton-proton collisions collected by the LHCb experiment. Decays involving a or meson are analysed by reconstructing only the or decay products. This paper presents the first measurement of and , and the most precise measurement of the other five asymmetries. There is no evidence of violation in any of the analysed decays. Additionally, two ratios between branching fractions of selected decays are measured
Resource-bounded Information Extraction: Acquiring Missing Feature Values On Demand
We present a general framework for the task of extracting specific information ``on demand\u27\u27 from a large corpus such as the Web under resource-constraints. Given a database with missing or uncertain information, the proposed system automatically formulates queries, issues them to a search interface, selects a subset of the documents, extracts the required information from them, and fills the missing values in the original database. We also exploit inherent dependency within the data to obtain useful information with fewer computational resources. We build such a system in the citation database domain that extracts the missing publication years using limited resources from the Web. We discuss a probabilistic approach for this task and present first results. The main contribution of this paper is to propose a general, comprehensive architecture for designing a system adaptable to different domains
Distinct chains for different instances: an effective strategy for multi-label classifier chains
Multi-label classification (MLC) is a predictive problem in which an object may be associated with multiple labels. One of the most prominent MLC methods is the classifier chains (CC). This method induces q binary classifiers, where q represents the number of labels. Each one is responsible for predicting a specific label. These q classifiers are linked in a chain, such that at classification time each classifier considers the labels predicted by the previous ones as additional information. Although the performance of CC is largely influenced by the chain ordering, the original method uses a random ordering. To cope with this problem, in this paper we propose a novel method which is capable of finding a specific and more effective chain for each new instance to be classified. Experiments have shown that the proposed method obtained, overall, higher predictive accuracies than the well-established binary relevance, CC and CC ensemble methods