ICAR Research Data Repository for Knowledge Management
An Improved Random Forest Classifier for multi-class classification
KRISHI: Publication and Data Inventory Repository
View Archive Info| Field | Value | |
| Title |
An Improved Random Forest Classifier for multi-class classification
Not Available |
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| Creator |
Archana Chaudhary
Savita Kolhe Raj Kamal |
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| Subject |
Groundnut disease, Improved-RFC, Machine learning, Multi-class classification
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| Description |
Not Available
The paper presents an improved-RFC (Random Forest Classifier) approach for multi-class disease classification problem. It consists of a combination of Random Forest machine learning algorithm, an attribute evaluator method and an instance filter method. It intends to improve the performance of Random Forest algorithm. The performance results confirm that the proposed improved-RFC approach performs better than Random Forest algorithm with increase in disease classification accuracy up to 97.80% for multi-class groundnut disease dataset. The performance of improved-RFC approach is tested for its efficiency on five benchmark datasets. It shows superior performance on all these datasets Not Available |
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| Date |
2018-11-12T10:03:54Z
2018-11-12T10:03:54Z 2016-09-01 |
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| Type |
Research Paper
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| Identifier |
Not Available
Not Available http://krishi.icar.gov.in/jspui/handle/123456789/10460 |
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| Language |
English
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| Relation |
Not Available;
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| Publisher |
Elsevier Pub
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