Få studier om ljudsymbolik, d.v.s. kopplingen mellan ords form och betydelse, har baserats på statistisk analys. I denna studie använder vi random forests med måttet permutation variable importance för att utforska vilka fonem (språkljud) som är prevalenta i engelska ord som beskriver hårdhet eller mjukhet. Denna icke-parametriska maskininlärningsmetod har funnits vara användbar för identifiering av ett fåtal inflytelserika förklaringsvariabler i situationer där n < p eller interkorrelationer förekommer. Vårt material och val av metod grundar sig på en tidigare studie, som fann att r-ljud hade starkt samband med betydelsen ‘strävhet’, men som inte kontrollerade för betydelsen ‘hårdhet’ trots att dessa korrelerar med varandra. Vi kontrollerar för dimensionen strävhet-lenhet genom att utföra random forest-analysen på två delmängder: ord som används för att beskriva hårdhet eller mjukhet (n = 81), samt den delmängd av dessa ord som inte beskriver strävhet eller lenhet (n  = 40). Samtliga regressorer är binära variabler, som anger förekomsten eller avsaknaden av varsitt fonem; vi utförde separata analyser på respektive datamängd för att se vilka fonem som hade störst effekt, då man betraktade specifika stavelsekomponenter. Vi fann att r-ljuden hade starkt samband med betydelsen ‘hårdhet’ både före och efter kontrollen för ‘strävhet’. Vi fann även att ljudet med symbolen i (t.ex. sista vokalen i fluffy) hade starkt samband med betydelsen ‘mjukhet’ före och efter kontroll, men vi misstänker att detta egentligen reflekterar sambandet mellan ‘mjukhet’ och exkluderade bakgrundsvariabler. / Few studies about sound symbolism, i.e. the association between the shape and meaning of words, have been based on statistical analysis. In this study, we use random forests and the permutation variable importance measure to explore which phonemes (language sounds) are prevalent in English descriptors of hardness or softness. This non-parametric machine learning method has been found useful for identification of a few influential predictors in situations where n < p or intercorrelations are present. Our materials and choice of method are based on an earlier study, in which a strong association was found between r-sounds and ‘roughness’, but which did not control for the meaning ‘hardness’ despite the correlation between them. We control for the dimension ‘roughness-smoothness’ by performing the random forest-analysis on two subsets of data: descriptors of hardness or softness (n = 81), and descriptors of hardness or softness which are not used to describe roughness or smoothness (n = 40). All regressors are binary variables indicating the presence or absence of a phoneme. Separate analyses were conducted on each subset to see which phonemes had the largest effect when specific syllable compontents were considered. We found that r-sounds had a strong association with ‘hardness’ both before and after controlling for ‘roughness’. We also found that the sound here symbolized by i (e.g. the last vowel of fluffy) had a strong association with ‘softness’ before and after control, but we suspect that this might instead reflect an association between ‘softness’ and excluded variables.

Multivariate and multiblock data analysis involves useful methodologies for analyzing large data sets in chemistry, biology, psychology, economics, sensory science, and industrial processes; among these methodologies, partial least squares (PLS) and orthogonal projections to latent structures (OPLS®) have become popular. Due to the increasingly computerized instrumentation, a data set can consist of thousands of input variables which contain latent information valuable for research and industrial purposes. When analyzing a large number of data sets (blocks) simultaneously, the number of variables and underlying connections between them grow very much indeed; at this point, reducing the number of variables keeping high interpretability becomes a much needed strategy. The main direction of research in this thesis is the development of a variable selection method, based on variable influence on projection (VIP), in order to improve the model interpretability of OnPLS models in multiblock data analysis. This new method is called multiblock variable influence on orthogonal projections (MB-VIOP), and its novelty lies in the fact that it is the first multiblock variable selection method for OnPLS models. Several milestones needed to be reached in order to successfully create MB-VIOP. The first milestone was the development of a single-block variable selection method able to handle orthogonal latent variables in OPLS models, i.e. VIP for OPLS (denoted as VIPOPLS or OPLS-VIP in Paper I), which proved to increase the interpretability of PLS and OPLS models, and afterwards, was successfully extended to multivariate time series analysis (MTSA) aiming at process control (Paper II). The second milestone was to develop the first multiblock VIP approach for enhancement of O2PLS® models, i.e. VIPO2PLS for two-block multivariate data analysis (Paper III). And finally, the third milestone and main goal of this thesis, the development of the MB-VIOP algorithm for the improvement of OnPLS model interpretability when analyzing a large number of data sets simultaneously (Paper IV). The results of this thesis, and their enclosed papers, showed that VIPOPLS, VIPO2PLS, and MB-VIOP methods successfully assess the most relevant variables for model interpretation in PLS, OPLS, O2PLS, and OnPLS models. In addition, predictability, robustness, dimensionality reduction, and other variable selection purposes, can be potentially improved/achieved by using these methods.

Estimating the variables of importance in inferentialmodelling is of significant interest in many fields of science,engineering, biology, medicine, finance and marketing. However,variable selection in high-dimensional data, where the number ofvariables is relatively large compared to the observed data points,is a major challenge and requires more research in order toenhance reliability and accuracy. In this bachelor thesis project,several known methods of variable selection, namely orthogonalmatching pursuit (OMP), ridge regression, lasso, adaptive lasso,elastic net, adaptive elastic net and multivariate adaptive regressionsplines (MARS) were implemented on a high-dimensional dataset.The aim of this bachelor thesis project was to analyze andcompare these variable selection methods. Furthermore theirperformance on the same data set but extended, with the numberof variables and observations being of similar size, were analyzedand compared as well. This was done by generating models forthe different variable selection methods using built-in packagesin R and coding in MATLAB. The models were then used topredict the observations, and these estimations were compared tothe real observations. The performances of the different variableselection methods were analyzed utilizing different evaluationmethods. It could be concluded that some of the variable selectionmethods provided more accurate models for the implementedhigh-dimensional data set than others. Elastic net, for example,was one of the methods that performed better. Additionally, thecombination of final models could provide further insight in whatvariables that are crucial for the observations in the given dataset, where, for example, variable 112 and 23 appeared to be ofimportance. / Att skatta vilka variabler som är viktigai inferentiell modellering är av stort intresse inom mångaforskningsområden, industrier, biologi, medicin, ekonomi ochmarknadsföring. Variabel-selektion i högdimensionella data, därantalet variabler är relativt stort jämfört med antalet observeradedatapunkter, är emellertid en stor utmaning och krävermer forskning för att öka trovärdigheten och noggrannheteni resultaten. I detta projekt implementerades ett flertal kändavariabel-selektions-metoder, nämligen orthogonal matching pursuit(OMP), ridge regression, lasso, elastic net, adaptive lasso,adaptive elastic net och multivariate adaptive regression splines(MARS), på ett högdimensionellt data-set. Syftet med dettakandidat-examensarbete var att analysera och jämföra resultatenav dessa metoder. Vidare analyserades och jämfördes metodernasresultat på samma data-set, fast utökat, med antalet variableroch observationer ungefär lika stora. Detta gjordes genom attgenerera modeller för de olika variabel-selektions-metodernavia inbygga paket i R och programmering i MATLAB. Dessamodeller användes sedan för att prediktera observationer, ochestimeringarna jämfördes därefter med de verkliga observationerna.Resultaten av de olika variabel-selektions-metodernaanalyserades sedan med hjälp av ett flertal evaluerings-metoder.Det kunde fastställas att vissa av de implementerade variabelselektions-metoderna gav mer relevanta modeller för datanän andra. Exempelvis var elastic net en av metoderna sompresterade bättre. Dessutom drogs slutsatsen att kombineringav resultaten av de slutgiltiga modellerna kunde ge en djupareinsikt i vilka variabler som är viktiga för observationerna, där,till exempel, variabel 112 och 23 tycktes ha betydelse. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

The Random Forest model is commonly used as a predictor function and the model have been proven useful in a variety of applications. Their popularity stems from the combination of providing high prediction accuracy, their ability to model high dimensional complex data, and their applicability under predictor correlations. This report investigates the random forest variable importance measure (VIM) as a means to find a ranking of important variables. The robustness of the VIM under imputation of categorical noise, and the capability to differentiate informative predictors from non-informative variables is investigated. The selection of variables may improve robustness of the predictor, improve the prediction accuracy, reduce computational time, and may serve as a exploratory data analysis tool. In addition the partial dependency plot obtained from the random forest model is examined as a means to find underlying relations in a non-linear simulation study. / Random Forest (RF) är en populär prediktormodell som visat goda resultat vid en stor uppsättning applikationsstudier. Modellen ger hög prediktionsprecision, har förmåga att modellera komplex högdimensionell data och modellen har vidare visat goda resultat vid interkorrelerade prediktorvariabler. Detta projekt undersöker ett mått, variabel importance measure (VIM) erhållna från RF modellen, för att beräkna graden av association mellan prediktorvariabler och målvariabeln. Projektet undersöker känsligheten hos VIM vid kvalitativt prediktorbrus och undersöker VIMs förmåga att differentiera prediktiva variabler från variabler som endast, med aveende på målvariableln, beskriver brus. Att differentiera prediktiva variabler vid övervakad inlärning kan användas till att öka robustheten hos klassificerare, öka prediktionsprecisionen, reducera data dimensionalitet och VIM kan användas som ett verktyg för att utforska relationer mellan prediktorvariabler och målvariablel.

Taxonomic classification has always been important to the study of any biological system. Many biological species will go unclassified and become lost forever at the current rate of classification. The current state of computer technology makes image storage and retrieval possible on a global level. As a result, computer-aided taxonomy is now possible. Content based image retrieval techniques utilize visual features of the image for classification. By utilizing image content and computer technology, the gap between taxonomic classification and species destruction is shrinking. This content based study utilizes the Fourier Descriptors of fifteen known landmark features on three Carpiodes species: C.carpio, C.velifer, and C.cyprinus. Classification analysis involves both unsupervised and supervised machine learning algorithms. Fourier Descriptors of the fifteen known landmarks provide for strong classification power on image data. Feature reduction analysis indicates feature reduction is possible. This proves useful for increasing generalization power of classification.