GENESIM : genetic extraction of a single, interpretable model

Models obtained by decision tree induction techniques excel in being interpretable.However, they can be prone to overfitting, which results in a low predictive performance. Ensemble techniques are able to achieve a higher accuracy. However, this comes at a cost of losing interpretability of the resulting model. This makes ensemble techniques impractical in applications where decision support, instead of decision making, is crucial. To bridge this gap, we present the GENESIM algorithm that transforms an ensemble of decision trees to a single decision tree with an enhanced predictive performance by using a genetic algorithm. We compared GENESIM to prevalent decision tree induction and ensemble techniques using twelve publicly available data sets. The results show that GENESIM achieves a better predictive performance on most of these data sets than decision tree induction techniques and a predictive performance in the same order of magnitude as the ensemble techniques. Moreover, the resulting model of GENESIM has a very low complexity, making it very interpretable, in contrast to ensemble techniques.Comment: Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex System

Low coherence digital holography microscopy based on the Lorenz-Mie scattering model

We demonstrate the use of low spatial and temporal coherence holography microscopy, based on the Lorenz-Mie model, using the standard tungsten-halogen lamp present in an inverted microscope. An optical model is put forward to incorporate the effect of spectral width and different incidence angles of the incident light determined by the aperture at the back focal plane of the condenser lens. The model is validated for 899 nm diameter polystyrene microspheres in glycerol, giving a resolution of 0.4% for the index of refraction and 2.2% for the diameter of the particles

Fourier-bessel based image analysis for multi-parameter particle characterization

We demonstrate a novel particle characterization method based on decomposition of conventional microscopy images in Fourier-Bessel (FB) components. This allows the simultaneous measurement of size, refractive index, 3D position and orientation of single colloidal particles

GENDIS : genetic discovery of shapelets

In the time series classification domain, shapelets are subsequences that are discriminative of a certain class. It has been shown that classifiers are able to achieve state-of-the-art results by taking the distances from the input time series to different discriminative shapelets as the input. Additionally, these shapelets can be visualized and thus possess an interpretable characteristic, making them appealing in critical domains, where longitudinal data are ubiquitous. In this study, a new paradigm for shapelet discovery is proposed, which is based on evolutionary computation. The advantages of the proposed approach are that: (i) it is gradient-free, which could allow escaping from local optima more easily and supports non-differentiable objectives; (ii) no brute-force search is required, making the algorithm scalable; (iii) the total amount of shapelets and the length of each of these shapelets are evolved jointly with the shapelets themselves, alleviating the need to specify this beforehand; (iv) entire sets are evaluated at once as opposed to single shapelets, which results in smaller final sets with fewer similar shapelets that result in similar predictive performances; and (v) the discovered shapelets do not need to be a subsequence of the input time series. We present the results of the experiments, which validate the enumerated advantages

MINDWALC : mining interpretable, discriminative walks for classification of nodes in a knowledge graph

Background Leveraging graphs for machine learning tasks can result in more expressive power as extra information is added to the data by explicitly encoding relations between entities. Knowledge graphs are multi-relational, directed graph representations of domain knowledge. Recently, deep learning-based techniques have been gaining a lot of popularity. They can directly process these type of graphs or learn a low-dimensional numerical representation. While it has been shown empirically that these techniques achieve excellent predictive performances, they lack interpretability. This is of vital importance in applications situated in critical domains, such as health care. Methods We present a technique that mines interpretable walks from knowledge graphs that are very informative for a certain classification problem. The walks themselves are of a specific format to allow for the creation of data structures that result in very efficient mining. We combine this mining algorithm with three different approaches in order to classify nodes within a graph. Each of these approaches excels on different dimensions such as explainability, predictive performance and computational runtime. Results We compare our techniques to well-known state-of-the-art black-box alternatives on four benchmark knowledge graph data sets. Results show that our three presented approaches in combination with the proposed mining algorithm are at least competitive to the black-box alternatives, even often outperforming them, while being interpretable. Conclusions The mining of walks is an interesting alternative for node classification in knowledge graphs. Opposed to the current state-of-the-art that uses deep learning techniques, it results in inherently interpretable or transparent models without a sacrifice in terms of predictive performance

Körperliche Leistungsfähigkeit von professionellen Feuerwehrmännern

The purpose of this study is twofold: (1) screening the physical fitness of a professional fire-brigade and (2) testing the influence of a training program on the physical fitness of professional firemen. The sample consisted of 95 professional firemen (42.0 ± 9.1 years) of a professional fire-brigade in Belgium. Stature and body mass were measured. Body composition was estimated by bioelectrical impedance analysis (BIA). Physical condition was determined using the EUROFIT test battery. During 4 months the subjects performed an exercise program twice a week. High values for body mass index (BMI) (27.1±3.3kg/m2) and percentage of body fat (26.0±4.9%), and a low score for endurance capacity were observed. Correlation analyses demonstrate that motor capacity decreases with ageing. Analysing the differences (Test session 2 - Test session 1) indicates that there were no significant changes in physical fitness over the 4-month training period. Comparing the two groups with the different number of training sessions attended did not reveal any significant differences between the two groups. It can be concluded that (1) the firemen of the fire-brigade under study were rather old, had a high BMI and a high percentage of body fat; (2) the performance at the endurance shuttle run did not reach the anticipated standard; (3) the training program did not gain the desired results.Uvod Posao vatrogasca jedan je od tjelesno najzahtjevnijih. Ekstremno visoki profesionalni fizički zahtjevi rezultiraju velikim brojem ozljeda na radu, pa čak i visokom incidencijom prijevremene smrti. Očigledno je, promatra li se sigurnost vatrogasca u odnosu na ostatak populacije, da vatrogasci trebaju biti u odličnoj tjelesnoj kondiciji da bi bili sposobni obavljati tako zahtjevan posao. Potrebno je uravnotežiti profesionalna opterećenja s mogućnostima pojedinog vatrogasca, što je određeno različitim faktorima, kao što su dob, antropometrijske karakteristike (postotak masnog tkiva, bezmasna tjelesna masa, indeks tjelesne mase) i motoričke sposobnosti (snaga i izdržljivost). Pokazalo se da su vatrogasni zadaci, kao što su penjanje do požarnih izlaza, poslovi na dizalici, razvaljivanje ulaza i spašavanje žrtava, značajno povezani s nekim čimbenicima kao što su visina, tjelesna masa, bezmasna tjelesna masa, postotak masnog tkiva, snaga stiska šake, broj izvedenih sklekova, broj izvedenih “trbušnjaka” i izdržljivost. Istraživači u raznim zemljama (npr. Francuskoj, Kanadi, SAD-u) dosta su proučavali fizičku pripremu vatrogasaca. Svaki vatrogasac trenira barem dva puta tjedno kako bi unaprijedio svoju mišićnu snagu, izdržljivost i kardiovaskularni status te kako bi smanjio postotak masnog tkiva i povecao mišićnu masu Te su aktivnosti nužne za održanje fizičke pripremljenosti vatorgasaca. U Belgiji gotovo da i nema istraživanja koja su se bavila vatrogascima. Svrha ovog istraživanja je dvostruka: (1) ispitati fizičku pripremljenost profesionalne vatrogasne jedinice i (2) provjeriti utjecaj trenažnog programa na razinu fizičke pripremljenosti te grupe. Metoda Uzorak ispitanika činilo je 95 profesionalnih vatrogasaca (42.0 ± 9.1 god.). Izmjerena im je tjelesna visina i tjelesna masa. Sastav tijela procijenjen je pomocu BIA. Tjelesna sposobnost određena im je uz pomoć baterije testova EUROFIT. Kroz period od četiri mjeseca ispitanici (N=81) su sudjelovali u programu vježbanja u okviru kojega su radili na unapređenju različitih komponenata fizičke pripreme. Vatrogasci su vježbali dva puta tjedno po 45 minuta pod vodstvom dva trenera. Svaki je trening bio isplaniran tako da je naglasak bio na razvoju različite komponente fizičke pripremljenosti, ali nije bilo ciljanog treninga glede različitih testova baterije EUROFIT. Pojedini treninzi bili su organizirani u okviru dva radna sata u vatrogasnoj stanici. Kako pohađanje treninga nije bilo obvezno, broj ispitanika po pojedinom treningu varirao je od 0 do 23, u prosjeku 13 po treningu. Prisustvovanje treninzima se bilježilo tako da se taj faktor mogao uzeti u obzir u okviru analize rezultata. Rezultati Izmjerene su visoke vrijednosti BMI (27.1±3.3 kg/m2) i postotka masnog tkiva (26.0±4.9%), kao i niska razina tjelesne izdržljivosti. Korelacijska analiza pokazala je da se motorički kapacitet smanjuje sa životnom dobi. Analiza razlika pokazuje da nema statistički značajnih razlika u tjelesnoj sposobnosti nakon provedenog četveromjesečnog trenažnog programa u odnosu na inicijalno stanje. Usporedba dviju grupa s obzirom na razlicit broj ispitanika koji su prisustvovali pojedinim treninzima nije pokazala statistički značajne razlike u tjelesnoj pripremljenosti. Zaključak Može se zaključiti da su (1) vatrogasci ispitivane vatrogasne jedinice prilično stari, visokih vrijednosti indeksa tjelesne mase te imaju visok postotak masnog tkiva; (2) ispitanici nisu postigli zadane norme u primjenjivanom zadatku izdržljivosti, (3) trenažni program nije polučio željene rezultate.Das Ziel dieser Studie war zweifach: die körperliche Leistungsfähigkeit von professionellen Feuerwehrmännern zu überprüfen, und 2) den Einfluss eines Trainingsprogramms auf die körperliche Leistungsfähigkeit von professionellen Feuerwehrmännern zu testen. 95 professionelle Feuwerwehrmänner (die 42,0±9,1 Jahre alt waren) aus einer professionellen Feuerwehr aus Belgien nahmen an dieser Forschung teil. Es wurden sowohl die Statur und als auch die Hörpermasse gemessen. Die Hörperzusammensetzung wurde mittels der bioelektrischen Impedanzanalyse (BIA) geschätzt. Die körperliche Leistungsfähigkeit wurde mittels der EUROFIT-Testbatterie festgestellt. Während des viermonatigen Zeitraumes nahmen die Probanden zweimal pro Woche an einem Übungsprogram teil. Es wurden hohe Werte des Hörpermassenindexes (27,1±3,3kg/m2) und des Anteils vom Hörperfett (26,0±4,9%) erhalten, aber die Werte der Ausdauerkapazität waren niedrig. Die Horrelationsanalysen zeigten, dass sich die motorische Fähigkeit mit dem Alter verringert. Die Analyse von Unterschieden (Testmessung 2 – Testmessung 1) zeigte, dass bei den Feuerwehrmännern keine bedeutende Unterschiede in der körperliche Leistungsfähigkeit nach dem viermonatigen Trainingszeitraum entstanden. Der Vergleich von zwei Gruppen, die unterschiedlich oft an den Trainingsstunden teilgenommen haben, zeigte keine bedeutenden Unterschiede zwischen den Gruppen. Aufgrund der durchgeführten Studie sind die folgenden Schlussfolgerungen möglich: 1) Die Feuerwehrmänner, die an der Forschung teilgenommen haben, waren ziemlich alt, sie hatten sowohl einen hohen Hörpermassenindex als auch den hohen Anteil vom Hörperfett. 2) Die Ergebnisse des Ausdauer- Pendellaufes haben die antizipierte Norm nicht erreicht. 3) Das Trainingsprogram hat die gewünschten Ergebnisse nicht erreicht