Ensemble of optimal trees, random forest and random projection ensemble classification
نویسندگان
چکیده
منابع مشابه
Random Projection Ensemble Classifiers
We introduce a novel ensemble model based on random projections. The contribution of using random projections is two-fold. First, the randomness provides the diversity which is required for the construction of an ensemble model. Second, random projections embed the original set into a space of lower dimension while preserving the dataset’s geometrical structure to a given distortion. This reduc...
متن کاملRandom Forest Ensemble Visualization
The Random forest model for machine learning has become a very popular data mining algorithm due to its high predictive accuracy as well as simiplicity in execution. The downside is that the model is difficult to interpret. The model consists of a collection of classification trees. Our proposed visualization aggregates the collection of trees based on the number of feature appearances at node ...
متن کاملFast Constrained Spectral Clustering and Cluster Ensemble with Random Projection
Constrained spectral clustering (CSC) method can greatly improve the clustering accuracy with the incorporation of constraint information into spectral clustering and thus has been paid academic attention widely. In this paper, we propose a fast CSC algorithm via encoding landmark-based graph construction into a new CSC model and applying random sampling to decrease the data size after spectral...
متن کاملRandom-forest-ensemble-based Classification of High-resolution Remote Sensing Images and Ndsm over Urban Areas
As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3...
متن کاملRandom Projection Trees Revisited
The Random Projection Tree (RPTREE) structures proposed in [1] are space partitioning data structures that automatically adapt to various notions of intrinsic dimensionality of data. We prove new results for both the RPTREE-MAX and the RPTREE-MEAN data structures. Our result for RPTREE-MAX gives a nearoptimal bound on the number of levels required by this data structure to reduce the size of it...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Advances in Data Analysis and Classification
سال: 2019
ISSN: 1862-5347,1862-5355
DOI: 10.1007/s11634-019-00364-9