Pregled bibliografske jedinice broj: 652058
Performance of machine learning methods in classification models with high-dimensional data
Performance of machine learning methods in classification models with high-dimensional data // Proceedings / Zadnik Stirn, Lidija ; Žerovnik, Janez ; Povh, Janez ; Drobne, Samo ; Lisec Anka (ur.).
Dolenjske Toplice: Slovensko društvo informatika, 2013. str. 219-224 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 652058 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Performance of machine learning methods in classification models with high-dimensional data
Autori
Zekić-Sušac, Marijana ; Pfeifer, Sanja ; Šarlija, Nataša
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings
/ Zadnik Stirn, Lidija ; Žerovnik, Janez ; Povh, Janez ; Drobne, Samo ; Lisec Anka - Dolenjske Toplice : Slovensko društvo informatika, 2013, 219-224
ISBN
978-961-6165-40-2
Skup
12th International Symposium on Operational Research
Mjesto i datum
Dolenjske Toplice, Slovenija, 25.09.2013. - 27.09.2013
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
machine learning; support vector machines; artificial neural networks; CART classification trees; k-nearest neighbour; large-dimensional data; cross-validation
Sažetak
The paper investigates the performance of four machine learning methods: artificial neural networks, classification trees, support vector machines, and k-nearest neighbour in classification type of problem by using a real dataset on entrepreneurial intentions of students. The aim is to find out which of the machine learning methods is more efficient in modelling high-dimensional data in the sense of the average classification rate obtained in a 10-fold cross-validation procedure. In addition, sensitivity and specificity is also observed. The results show that the accuracy of artificial neural networks is significantly higher than the accuracy of k-nearest neighbour, but the difference among other methods is not statistically significant.
Izvorni jezik
Engleski
Znanstvena područja
Ekonomija
POVEZANOST RADA
Projekti:
010-0101195-0872 - Transformacija poduzetničkog potencijala u poduzetničko ponašanje (Pfeifer, Sanja, MZOS ) ( CroRIS)
010-0101195-1048 - Modeli za ocjenu rizičnosti poslovanja poduzeća (Šarlija, Nataša, MZOS ) ( CroRIS)
Ustanove:
Ekonomski fakultet, Osijek
