Abstract
This research proposed ways with comparison results for feature selection and reduction for plant's leaf classification based on a key concept that features in a data set may include weakly relevant or redundant features. Six classifiers of support vector machine (SVM) model are demonstrated with ten features of about 320 leaves of two basil species sharing common genus. Plant species in a common genus typically have various aspects of similarity in their leaf features and this is our challenge in the way whether feature reduction should be done. Feature reduction provides the decrease in processing time in many cases, but it can easily reduce classification performance in terms of accuracy rate. According to our proposed techniques, an optimal feature reduction can still obtain while we still gain a perfect classification of 100 percent of accuracy.
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Claims 3
Claim · model-written summary
Feature reduction decreased processing time in many cases.
Verbatim
t0“Two Basil Species”
e0“Feature reduction provides the decrease in processing time in many cases”
Claim · model-written summary
Feature reduction could reduce classification accuracy.
- Taxon
- Two Basil Speciest0
- Outcome
- classification performance in terms of accuracy ratee0
- Direction
- reducee0
Not reportedComparatorValue
Verbatim
t0“Two Basil Species”
e0“it can easily reduce classification performance in terms of accuracy rate”
Claim · model-written summary
The proposed optimal feature reduction achieved 100 percent classification accuracy.
Verbatim
t0“Two Basil Species”
e0“our proposed techniques, an optimal feature reduction can still obtain while we still gain a perfect classification of 100 percent of accuracy”