All Shops

Go to British Wildlife

6 issues per year 84 pages per issue Subscription only

British Wildlife is the leading natural history magazine in the UK, providing essential reading for both enthusiast and professional naturalists and wildlife conservationists. Published six times a year, British Wildlife bridges the gap between popular writing and scientific literature through a combination of long-form articles, regular columns and reports, book reviews and letters.

Subscriptions from £25 per year

Conservation Land Management

4 issues per year 44 pages per issue Subscription only

Conservation Land Management (CLM) is a quarterly magazine that is widely regarded as essential reading for all who are involved in land management for nature conservation, across the British Isles. CLM includes long-form articles, events listings, publication reviews, new product information and updates, reports of conferences and letters.

Subscriptions from £18 per year
Academic & Professional Books  Reference  Data Analysis & Modelling  Data Analysis & Statistics

Statistical Methods for Imbalanced Data in Ecological and Biological Studies

Handbook / Manual Coming Soon
By: Osamu Komori(Author), Shinto Eguchi(Author)
Publisher: Springer-Verlag
Statistical Methods for Imbalanced Data in Ecological and Biological Studies
Click to have a closer look
Select version
  • Statistical Methods for Imbalanced Data in Ecological and Biological Studies ISBN: 9784431555698 Paperback 2019 Available for pre-order : Due Sep 2019
    £44.99
    #223243
Selected version: £44.99
About this book Contents Customer reviews Related titles

About this book

Statistical Methods for Imbalanced Data in Ecological and Biological Studies presents a fresh, new approach in that it provides a comprehensive recent review of challenging problems caused by imbalanced data in prediction and classification, and also in that it introduces several of the latest statistical methods of dealing with these problems. The book discusses the property of the imbalance of data from two points of view. The first is quantitative imbalance, meaning that the sample size in one population highly outnumbers that in another population. It includes presence-only data as an extreme case, where the presence of a species is confirmed, whereas the information on its absence is uncertain, which is especially common in ecology in predicting habitat distribution. The second is qualitative imbalance, meaning that the data distribution of one population can be well specified whereas that of the other one shows a highly heterogeneous property. A typical case is the existence of outliers commonly observed in gene expression data, and another is heterogeneous characteristics often observed in a case group in case-control studies. The extension of the logistic regression model, maxent, and AdaBoost for imbalanced data is discussed, providing a new framework for improvement of prediction, classification, and performance of variable selection. Weights functions introduced in the methods play an important role in alleviating the imbalance of data. This book also furnishes a new perspective on these problem and shows some applications of the recently developed statistical methods to real data sets.

Contents

1. Imbalance Data
2. Weighted Logistic Regression
3. Beta-Maxent
4. Generalized-t Statistic
5. Machine Learning Methods for Imbalance Data

Customer Reviews

Handbook / Manual Coming Soon
By: Osamu Komori(Author), Shinto Eguchi(Author)
Publisher: Springer-Verlag
Current promotions
Spring PromotionsPelagic PublishingOrder your free copy of our 2018 equipment catalogueBritish Wildlife