Solving sampling bias problems in presence–absence or presence?only species data using zero?inflated models

Bias correction of bounded location errors in presenceonly data

presence-only data Trevor J. Hefley* , BrianM. Brost andMevin B. Hooten Department of Statistics, Kansas State University, Manhattan, KS, USA; Marine Mammal Laboratory, Alaska Fisheries Science Center, National Oceanic and Atmospheric Administration, Seattle, WA, USA; and U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Department of Fish, Wildlife, and Conservation...

Solving Data Clustering Problems using Chaos Embedded Cat Swarm Optimization

In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one ...

Solving Data Clustering Problems using Chaos Embedded Cat Swarm Optimization

In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one ...

Nutcracker, neurosis, or sampling bias?

Solving Regression Problems Using Competitive Ensemble Models

The use of ensemble models in many problem domains has increased significantly in the last few years. The ensemble modeling, in particularly boosting, has shown a great promise in improving predictive performance of a model. Combining the ensemble members is normally done in a co–operative fashion where each of the ensemble members performs the same task and their predictions are aggregated to ...