Usporedba uspješnosti različitih algoritama strojnog učenja na različitim skupovima podataka

undergraduate thesis

• Title (english): Comparing Efficiency of Various Machine Learning Algorithms on Different Datasets
• Author: Igor Šarić
• Mentor: Marin Šilić (mentor)
• Committee member: Marin Šilić (predsjednik povjerenstva)
• Committee member: Klemo Vladimir (član povjerenstva)
• Committee member: Goran Delač (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2018-07-13, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Cilj je usporediti algoritme strojnog učenja koji rješavaju problem klasifikacije na različitim skupovima podatka. Usporedba se temelji na točnosti predviđanja algoritama na njima prije neviđenim podacima. Algoritmi koji se uspoređuju su: Naivni Bayesov klasifikator, Stablo odluke, Najbliži susjed, Linearna diskriminantna analiza, Logistička regresija i Stroj potpornih vektora. Usporedba se provodila nad tri različita skupa podataka. Rezultati usporedbe su pokazali da algoritmi Stablo odluke i... More Najbliži susjed imaju dobru točnost neovisno o vrsti podataka, dok algoritmi Linearne diskriminantne analize, Logističke regresije i Stroja potpornih vektora imaju dobre i bolje rezultate za specifičnije vrste podatka. Less

Abstract (english)

The goal is to compare different machine learning algorithms for classification problems using different datasets. The comparison is based on predicting the classes of data not previously seen by the algorithms. Algorithms used in the comparison are: Naive Bayes classifier, Decision tree, Nearest neighbour, Linear discriminant analysis, Logistic regression, Support vector machine. Three different datasets were used for the comparison. Results of the comparison show that accuracies of Nearest... More neighbour and Decision tree algorithms are good, no matter the data type, while algorithms like Linear discriminant analysis, Logistic regression and Support vector machine have better result for specific data types. Less

• Keywords: Strojno učenje; usporedba uspješnosti; klasifikacija; Naivni Bayesov kalsifkator; Stablo odluke; Najbliži susjed; Linearna diskriminantna analiza; Logistička regresija; Stroj potpornih vektora
• Keywords (english): Machine learning; comparison; classification; Naive Bayes classifier; Decision tree; Nearest neighbour; Linear discriminant analysis; Logistic regression; Support vector machine
• Language: croatian
• URN:NBN: urn:nbn:hr:168:059567
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2019-03-14 01:08:25