Fires in urban-forest ecosystems (UFEs) are frequent with complex causes, posing a serious hazard to human lives and infrastructure. Thus, quantifying wildfire risks in UFEs and their spatial pattern is quintessential to develop appropriate fire management strategies. The aim of this study was to explore spatial (geographically weighted logistic regression, GWLR) versus non-spatial (logistic re...

Today, there are not many good measures for detecting influential observations in case of fitting a logistic regression model. So, the purpose this article is to extrapolate from pre-existing deletion diagnostics defined points multiple linear regression, i.e. DFFITS, DFBETAS and Cook's Distance scenario binary model then view multinomial as special same. The threshold determining whether an ob...

This is a Monte Carlo simulation-based study that explores the effect of the sample size of the background database on a likelihood ratio (LR)-based forensic text comparison (FTC) system built on multivariate authorship attribution features. The text messages written by 240 authors who were randomly selected from an archive of chatlog messages were used in this study. The strength of evidence (...

1 Extended Abstract This extended abstract describes the Berkeley 1 participation in the Domain Specific task for CLEF 2005. This year we submitted the minumum number of entries for each subtask (3 monolingual runs, 6 bilingual runs, and 3 multilingual runs). In our runs we employed retrieval algorithms data fusion methods that have performed relatively well in some other retrieval contexts, bu...

In this work, we make use of a supervised machine learning algorithm based on Logistic Regression (LR) to select TeV blazar candidates from the 4FGL-DR2 / 4LAC-DR2, 3FHL, 3HSP, and 2BIGB catalogs. LR constructs hyperplane selection optimal parameters, named features, hyper-parameters whose values control process determine features that ends up learning, discriminate blazars non-TeV blazars. add...

This article proposes a method for multiclass classification problems using ensembles of multinomial logistic regression models. A multinomial logit model is used as a base classifier in ensembles from random partitions of predictors. The multinomial logit model can be applied to each mutually exclusive subset of the feature space without variable selection. By combining multiple models the pro...

In binary classification problems it is common for the two classes to be imbalanced: one case is very rare compared to the other. In this paper we consider the infinitely imbalanced case where one class has a finite sample size and the other class’s sample size grows without bound. For logistic regression, the infinitely imbalanced case often has a useful solution. Under mild conditions, the in...