A5103032797- Machine Learning and Data Classification
- Imbalanced Data Classification Techniques
- Anomaly Detection Techniques and Applications
- Machine Learning and Algorithms
- Neural Networks and Applications
Chongqing University of Posts and Telecommunications
2021
Mitigating label noise is a crucial problem in classification. Noise filtering an effective method of dealing with which does not need to estimate the rate or rely on any loss function. However, most methods focus mainly binary classification, leaving more difficult counterpart multiclass classification relatively unexplored. To remedy this deficit, we present definition for setting and propose general framework novel learning Two examples complete random forest (mCRF) relative density, are...
This article presents a simple sampling method, which is very easy to be implemented, for classification by introducing the idea of random space division, called "random division sampling" (RSDS). It can extract boundary points as sampled result efficiently distinguishing label noise points, inner and points. makes it first general method that not only reduce data size but also enhance accuracy classifier, especially in label-noisy classification. The "general" means restricted any specific...
This paper presents an adaptive method to search best number of trees (Ntree) in complete random forest (CRF). Ada- CRF can automatically determine whether the has reached a stable state during establishment forest, thereby avoiding inaccurate results caused by too small Ntree, or low efficiency large Ntree. As general sampling method, Ada-CRF not only effectively compress amount data, but also filter label noise improve data quality. identify points searching for tree division data. To...