Testing modified confusion entropy as split criterion for decision trees

In 2010, a new performance measure to evaluate the results obtained by algorithms of data classification was presented, Confusion Entropy (CEN). This render measure is able to achieve a greater discrimination than Accuracy focusing on the distribution across different classes of both correctly and wrongly classified instances, but it is not able to work correctly in cases of binary classification. Recently, an enhancement has been proposed to correct its behaviour in those cases, the Modified Confusion Entropy (MCEN). In this work, we propose a new algorithm, MCENTree. This algorithm uses MCEN as splitting criterion to build a decision tree model instead of CEN, as proposed in the CENTree algorithm in the literature. We make a comparison among a classic J48, CENTree and the new algorithm MCENTree in terms of Accuracy, CEN and MCEN performance measures, and we analyze how the undesired behaviour of CEN affects the results of the algorithms and how MCEN shows a good behaviour in terms of results: while MCENTree gives correct results in a statistical range [0,1], CENTree sometimes gives non monotonous and out of range results in binary class classification

Assessment of machine learning algorithm-based grading of Populus x euramericana I-214 structural sawn timber

The efficiency of visual grading standards applied to structural timber is often inappropriate, and timber properties are either under or over-graded. Although not included in the current UNE 56544 visual grading standard, machine learning algorithms represent a promising alternative to grade structural timber. The general aim of this research was to compare the performance of machine learning algorithms based on visual defects, non-destructive techniques and sawing systems (“cut type”) with UNE 56544:1997 visual grading in order to predict the qualifying efficiency of Populus x euramericana I-214 structural timber. Visual evaluation, ultrasound and vibrational non-destructive testing, and sawing systems register (radial, tangential and mixed) were applied to characterize 945 beams. In addition, in order to retrieve actual physical-mechanical values, density and static bending destructive testing (EN-408:2011 + A1:2012) was also carried out. Several machine learning algorithms were then used to grade the beams, and their predictive accuracy was compared with that of visual grading. To do so, three scenarios were considered: a first scenario in which only visual variables were used; a second scenario in which “cut type” variables were also included; and a third scenario in which additional non-destructive variables were considered. Results showed a poor level of performance of UNE 56544:1997, with an apparent mismatch between the strength values assigned for each visual grade (established by the EN 338 standard) and the actual values. On the opposite, all algorithms performed better than visual grading and may thus be deemed as promising timber strength grading tools.Junta de Castilla y León (project VA047A08