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The clipped images were classified into a binary class: urban built-up and others.
A total of 44 binary class data sets and 15 multiclass imbalanced data sets are used to test the performance of the proposed method.
There exist numerous state of the art classification algorithms that are designed to handle the data with nominal or binary class labels.
While executing the bytecodes, bytecode grouping and rescheduling are done by a T-POC bytecode rescheduler to generate the new binary class images in memory.
The classifier was trained on a balanced binary class distribution.
Data were analyzed using principal component analysis and binary class comparison analyses.
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Our proposed method is tested on both binary-class and multi-class microarray datasets.
Fourth, extensions of binary-class AdaBoost to multiclass AdaBoost are described.
MIDA consists of two versions, i.e., binary-class MIDA (B-MIDA) and multi-class MIDA (M-MIDA), which are utilized to cope with binary-class (standard) and multi-class multiple-instance learning tasks, respectively.
The designed M3IFW algorithm can be applied to both standard binary-class multiple-instance learning and the corresponding multi-class learning, and we abbreviate them to B-M3IFW (binary-class M3IFW) and M-M3IFW (multi-class M3IFW), respectively.
Tables 10 and 11 state the results of experiments over the three binary-class data sets.
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