Implementacija strukture globokih gozdov na osnovi gcForest

KOŠIR, TIMOTEJ (author), Žabkar, Jure (mentor)

Nevronske mreže na področju strojnega učenja, predvsem pri obdelavi slik, besedil in zaporedij, prekašajo klasične modele. Njihova popularnost je prebudila poskuse iskanja alternativ. Kot ena izmed idej so se pojavili globoki gozdovi, ki jih avtorja vpeljeta v obliki strukture gcForest. Temelji na modelu naključnih gozdov, ki jih združujemo v kaskade. Struktura gcForest se odlikuje na učenju z majhnimi podatkovnimi množicami in ne potrebuje veliko računalniških virov. Raziščemo osnovne gradnike strukture in podamo spremembe, ki bi lahko izboljšale delovanje. Temu dodamo lastno implementacijo dreves, naključnih gozdov ter strukture s predlaganimi spremembami. Posamezne dele implementacije testiramo. Bolj se posvetimo primerjavi rezultatov z različico avtorjev, ki jo opravimo na štirih podatkovnih množicah. Ker dosežemo rahlo slabše rezultate, podamo razloge zanje. Govorimo tudi o naslednjih korakih implementacije.

• Language: Slovenian
• Keywords: gcForest, naključni gozdovi, globoko učenje, zlaganje
• Work type: Bachelor thesis/paper
• Typology: 2.11 - Undergraduate Thesis
• Organization: FRI - Faculty of Computer and Information Science
• Year: 2022
• PID: 20.500.12556/RUL-140562
• COBISS.SI-ID: 123848707
• Publication date in RUL: 15.09.2022

KOŠIR, TIMOTEJ, 2022, Implementacija strukture globokih gozdov na osnovi gcForest [online]. Bachelor’s thesis. [Accessed 19 May 2025]. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=140562

Secondary language

• Language: English
• Title: Implementation of deep forest structure based on gcForest

Abstract

In the field of machine learning, especially in image, text, and sequence processing, neural networks have had more success than classical models. The popularity of neural networks has led to several attempts to find an alternative. Deep forest structures have emerged as one of them. The authors have integrated the concept of deep forest into a model called gcForest. It is based on robust random forests that are connected into a cascade. Structure excels at learning from small data sets and does not consume many computer resources. We examine the building blocks of the structure and introduce changes that could improve performance. In the next step, we program a custom library for decision trees, random forests, and the structure with the proposed changes. Then we test the parts of the implementation separately and compare the results with those of the authors on four data sets. Since we obtain worse results, we provide an explanation. Finally, we talk about the next steps of the implementation.

• Keywords: gcForest, random forests, deep learning, stacking