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Abstract: Zanesljiva identifikacija kompleksnih vsebinskih struktur v primerih, kjer posamezni primerki podatkovnega nabora niso homogeni, pač pa združujejo informacije več virov, predstavlja enega izmed ključnih metodoloških izzivov sodobne podatkovne analitike. Relativno enostavna je namreč naloga, kjer je določen primerek homogen in ga z uporabo večrazredne klasifikacije znamo relativno preprosto razvrstiti v enega izmed ponujenih razredov. Kompleksnost pa se drastično poveča, ko se v istem primerku skriva več virov. V tem primeru osnovne metode analize ne zadostujejo več in potrebujemo naprednejše pristope, ki so sposobni razbrati soobstoj več razredov oziroma oznak, kar je tudi domena večznačne klasifikacije. V predloženi doktorski disertaciji obravnavamo omenjeni problem na področju metagenomike, ki med drugim omogoča raziskovanje mikrobiote, raznolike skupnosti bakterij in drugih mikroorganizmov v določenem okolju. Z naprednimi tehnikami sekvenciranja iz njih pridobimo zaporedja DNK celotne mikrobne združbe, ki jih lahko opišemo kot izjemno dolga besedila, zapisana z abecedo štirih nukleotidov: A, T, G in C. Naš cilj je v teh besedilih poiskati t. i. označevalne gene, ki so izključno ali močno povezani z gostiteljem. V ta namen smo na podlagi optimizacijskih pristopov in domenskih pravil predlagali metodo ekstrakcije značilnic, temelječo na osnovi k-merov, tj. krajših delov DNK. Pristop na osnovi k-merov se je izkazal za zelo učinkovitega, zato smo ga uporabili tudi pri sintetičnem generiranju vzorcev mikrobnih oziroma mikrobiotskih podatkov. Metoda temelji na pripravi profilov k-merov in na nanje osnovanih grafih prehodov. Ker smo v doktorski disertaciji analizirali lokacijsko specifične vzorce, smo morali njihov manjši nabor čistih vzorcev ustrezno razširiti. Še več, sintetično smo razširili tudi nabor mešanih vzorcev, kar predstavlja še večji izziv v realnih okoljih. Obe predlagani metodi sta se združili v konceptualno najzahtevnejšem delu doktorske naloge, predlagani metodi hierarhične večznačne klasifikacije na osnovi ekstrakcije značilnic, imenovani MLB. Z njo smo na osnovi vhodnih podatkov, tj. čistih ali sintetično ustvarjenih vzorcev, napovedovali deleže gostiteljev v mešanih mikrobnih vzorcih. Rezultate metode MLB smo primerjali s tistimi, pridobljenimi z orodjem SourceTracker, vodilnim orodjem za natančno identifikacijo in kvantifikacijo gostiteljev mikrobov v mešanih vzorcih. Metodi smo ovrednotili z uveljavljenimi metrikami na področju večznačne klasifikacije, ki razkrivajo, da metoda MLB učinkovito rešuje problem določitve gostiteljev in njihovih deležev ter poda primerljive, večinoma pa boljše rezultate kot orodje SourceTracker.
Keywords: strojno učenje, večznačna klasifikacija, ekstrakcija značilnic, obdelava naravnega jezika, mikrobiotski podatki
Published in DKUM: 20.10.2025; Views: 0; Downloads: 19
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Abstract: This paper defines time-series numerical association rule mining in smart-agriculture applications from an explainable-AI perspective. Two novel explainable methods are presented, along with a newly developed algorithm for time-series numerical association rule mining. Unlike previous approaches, such as fixed interval time-series numerical association, the proposed methods offer enhanced interpretability and an improved data science pipeline by incorporating explainability directly into the software library. The newly developed xNiaARMTS methods are then evaluated through a series of experiments, using real datasets produced from sensors in a smart-agriculture domain. The results obtained using explainable methods within numerical association rule mining in smart-agriculture applications are very positive.
Keywords: association rule mining, explainable artificial intelligence, XAI, numerical association rule mining, optimization algorithms
Published in DKUM: 27.08.2025; Views: 0; Downloads: 5
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Abstract: Uspešnost odločitvenih dreves temelji na predpostavki, da učni podatki za vsak razred vključujejo enako količino informacij. Pri nesorazmerni porazdelitvi razredov so klasifikatorji pristransko usmerjeni k večinskim razredom. Zaradi majhnega števila vzorcev manjšinskih razredov klasifikatorji niso zmožni ustreznega usvajanja znanja, kar vodi do slabšega posploševanja in prekomernega prileganja. V okviru zaključnega dela smo razvili več algoritmov za gradnjo uravnoteženih evolucijskih dreves, ki se osredotočajo na reševanje izzivov, povezanih z nesorazmerno porazdelitvijo razredov. Rezultati eksperimenta kažejo, da uravnoteženost evolucijskih dreves ne prispeva k izboljšanju klasifikacije v primerjavi s tradicionalnimi metodami.
Keywords: evolucijski algoritem, odločitvena drevesa, klasifikacija, neuravnoteženi podatki
Published in DKUM: 06.02.2025; Views: 0; Downloads: 83
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Abstract: Microbiota analysis can provide valuable insights in various fields, including diet and nutrition, understanding health and disease, and in environmental contexts, such as understanding the role of microorganisms in different ecosystems. Based on the results, we can provide targeted therapies, personalized medicine, or detect environmental contaminants. In our research, we examined the gut microbiota of 16 animal taxa, including humans, as well as the microbiota of cattle and pig manure, where we focused on 16S rRNA V3-V4 hypervariable regions. Analyzing these regions is common in microbiome studies but can be challenging since the results are high-dimensional. Thus, we utilized machine learning techniques and demonstrated their applicability in processing microbial sequence data. Moreover, we showed that techniques commonly employed in natural language processing can be adapted for analyzing microbial text vectors. We obtained the latter through frequency analyses and utilized the proposed hierarchical clustering method over them. All steps in this study were gathered in a proposed microbial sequence data processing pipeline. The results demonstrate that we not only found similarities between samples but also sorted groups’ samples into semantically related clusters. We also tested our method against other known algorithms like the Kmeans and Spectral Clustering algorithms using clustering evaluation metrics. The results demonstrate the superiority of the proposed method over them. Moreover, the proposed microbial sequence data pipeline can be utilized for different types of microbiota, such as oral, gut, and skin, demonstrating its reusability and robustness.
Keywords: machine learning, NLP, hierarchical clustering, microbial data, microbiome, n-grame
Published in DKUM: 04.09.2024; Views: 38; Downloads: 12
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Abstract: This study aimed to observe the impact of eight explainable AI (XAI) explanation techniques on user trust and satisfaction in the context of XAI-enhanced learning analytics while comparing two groups of STEM college students based on their Bologna study level, using various established feature relevance techniques, certainty, and comparison explanations. Overall, the students reported the highest trust in local feature explanation in the form of a bar graph. Additionally, master's students presented with global feature explanations also reported high trust in this form of explanation. The highest measured explanation satisfaction was observed with the local feature explanation technique in the group of bachelor's and master's students, with master's students additionally expressing high satisfaction with the global feature importance explanation. A detailed overview shows that the two observed groups of students displayed consensus in favored explanation techniques when evaluating trust and explanation satisfaction. Certainty explanation techniques were perceived with lower trust and satisfaction than were local feature relevance explanation techniques. The correlation between itemized results was documented and measured with the Trust in Automation questionnaire and Explanation Satisfaction Scale questionnaire. Master's-level students self-reported an overall higher understanding of the explanations and higher overall satisfaction with explanations and perceived the explanations as less harmful.
Keywords: explainable artificial intelligence, learning analytics, XAI techniques, trust, explanation satisfaction
Published in DKUM: 12.02.2024; Views: 368; Downloads: 68
Full text (3,24 MB)
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