Global and local distance-based generalized linear models

This paper introduces local distance-based generalized linear models. These models extend (weighted) distance-based linear models first to the generalized linear model framework. Then, a nonparametric version of these models is proposed by means of local fitting. Distances between individuals are the only predictor information needed to fit these models. Therefore, they are applicable, among others, to mixed (qualitative and quantitative) explanatory variables or when the regressor is of functional type. An implementation is provided by the R package dbstats, which also implements other distance-based prediction methods. Supplementary material for this article is available online, which reproduces all the results of this article.Peer ReviewedPostprint (author's final draft

A hybrid naïve Bayes based on similarity measure to optimize the mixed-data classification

In this paper, a hybrid method has been introduced to improve the classification performance of naïve Bayes (NB) for the mixed dataset and multi-class problems. This proposed method relies on a similarity measure which is applied to portions that are not correctly classified by NB. Since the data contains a multi-valued short text with rare words that limit the NB performance, we have employed an adapted selective classifier based on similarities (CSBS) classifier to exceed the NB limitations and included the rare words in the computation. This action has been achieved by transforming the formula from the product of the probabilities of the categorical variable to its sum weighted by numerical variable. The proposed algorithm has been experimented on card payment transaction data that contains the label of transactions: the multi-valued short text and the transaction amount. Based on K-fold cross validation, the evaluation results confirm that the proposed method achieved better results in terms of precision, recall, and F-score compared to NB and CSBS classifiers separately. Besides, the fact of converting a product form to a sum gives more chance to rare words to optimize the text classification, which is another advantage of the proposed method

Local Distance-Based Generalized Linear Models using the dbstats package for R

This paper introduces local distance-based generalized linear models. These models extend (weighted) distance-based linear models firstly with the generalized linear model concept, then by localizing. Distances between individuals are the only predictor information needed to fit these models. Therefore they are applicable to mixed (qualitative and quantitative) explanatory variables or when the regressor is of functional type. Models can be fitted and analysed with the R package dbstats, which implements several distancebased prediction methods

Estatistika metodoak distantzietan oinarrituriko ikuspegitik

Lan honetan analisi anizkoitzaren baitan biltzen diren distantzietan oinarrituriko hainbat metodoren berrikusketa egin da. Oinarrizko kontzeptuak aurkeztu dira lehenik, ondoren metodoen funtsa laburki azaldu ahal izateko. Zehazki, erregresioa, diskriminazio-analisia, cluster-analisia, tipikotasuna eta sakonera aztertzen dituzten metodoak bildu ditugu. Metodologia honek berezitasun nabarmena du, edozein datu motaren gainean aplikagarria baita, datuek erakusten duten banaketa zein den ezagutu beharrik gabe. Azkenik, metodo hauen erabilgarritasuna erakusteko, funtzio-datu errealen gainean aplikatu eta lortutako emaitzak azaldu dira