Fast Extraction Method of High-Level Feature Using Random Forests from Imbalanced Training Data

Loan Default Prediction on Large Imbalanced Data Using Random Forests

In this paper, we propose an improved random forest algorithm which allocates weights to decision trees in the forest during tree aggregation for prediction and their weights are easily calculated based on out-of-bag errors in training. Experiments results show that our proposed algorithm beats the original random forest and other popular classification algorithms such as SVM, KNN and C4.5 in t...

A Novel Feature Extraction Method with Feature Selection to Identify Golgi-Resident Protein Types from Imbalanced Data.

The Golgi Apparatus (GA) is a major collection and dispatch station for numerous proteins destined for secretion, plasma membranes and lysosomes. The dysfunction of GA proteins can result in neurodegenerative diseases. Therefore, accurate identification of protein subGolgi localizations may assist in drug development and understanding the mechanisms of the GA involved in various cellular proces...

Feature Extraction from Visual Data Feature Extraction from Visual Data

Predicting disease risks from highly imbalanced data using random forest

BACKGROUND We present a method utilizing Healthcare Cost and Utilization Project (HCUP) dataset for predicting disease risk of individuals based on their medical diagnosis history. The presented methodology may be incorporated in a variety of applications such as risk management, tailored health communication and decision support systems in healthcare. METHODS We employed the National Inpatie...

On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data

MOTIVATION Genome-wide association (GWA) studies have proven to be a successful approach for helping unravel the genetic basis of complex genetic diseases. However, the identified associations are not well suited for disease prediction, and only a modest portion of the heritability can be explained for most diseases, such as Type 2 diabetes or Crohn's disease. This may partly be due to the low ...