Laykin, S., Dept. of Industrial Engineering, Ben- Gurion University of the Negev, Beer-Sheva 84105, Israel Edan, Y., Dept. of Industrial Engineering, Ben- Gurion University of the Negev, Beer-Sheva 84105, Israel Alchanatis, V., Inst. of Agricultural Engineering, ARO, Volcani Center, Bet- Dagan 50250, Israel
This paper presents an on-line hierarchical classifier for agricultural products. The classifier consists of two levels. The first level detects new populations using an on-line clustering algorithm. The second level selects the best-fit classifier using a fuzzy system. This paper presents the combination of the two levels into a complete system. Feature selection is conducted on-line according to the classified population. A synthetic dataset is used to estimate the classifier capabilities and compare it to previous results. Results indicated that the combined online system results in improved classification accuracy.
On-line feature and classifier selection for agricultural produce
Laykin, S., Dept. of Industrial Engineering, Ben- Gurion University of the Negev, Beer-Sheva 84105, Israel Edan, Y., Dept. of Industrial Engineering, Ben- Gurion University of the Negev, Beer-Sheva 84105, Israel Alchanatis, V., Inst. of Agricultural Engineering, ARO, Volcani Center, Bet- Dagan 50250, Israel
On-line feature and classifier selection for agricultural produce
This paper presents an on-line hierarchical classifier for agricultural products. The classifier consists of two levels. The first level detects new populations using an on-line clustering algorithm. The second level selects the best-fit classifier using a fuzzy system. This paper presents the combination of the two levels into a complete system. Feature selection is conducted on-line according to the classified population. A synthetic dataset is used to estimate the classifier capabilities and compare it to previous results. Results indicated that the combined online system results in improved classification accuracy.