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2 2021

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Overview of feature selection methods used in malignant melanoma diagnostics

Michalska Magdalena

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

2021

T. 11, nr 1

32--35

Currently, a large number of trait selection methods are used. They are becoming more and more of interest among researchers. Some of the methods are of course used more frequently. The article describes the basics of selection-based algorithms. FS methods fall into three categories: filter wrappers, embedded methods. Particular attention was paid to finding examples of applications of the described methods in the diagnosis of skin melanoma.

Obecnie stosuje się wiele metod selekcji cech. Cieszą się coraz większym zainteresowaniem badaczy. Oczywiście niektóre metody są stosowane częściej. W artykule zostały opisane podstawy działania algorytmów opartych na selekcji. Metody selekcji cech należące dzielą się na trzy kategorie: metody filtrowe, metody opakowujące, metody wbudowane. Zwrócono szczególnie uwagę na znalezienie przykładów zastosowań opisanych metod w diagnostyce czerniaka skóry.

A statistical comparison of feature selectiontechniques for solar energy forecastingbased on geographical data

EL Motaki Salou, El Fengour Abdelhak

Computer Assisted Methods in Engineering and Science

2021

Vol. 28, no. 2

105--118

In recent years, solar energy forecasting has been increasingly embraced as a sustainablelow-energy solution to environmental awareness. It is a subject of interest to the scientificcommunity, and machine learning techniques have proven to be a powerful means toconstruct an automatic learning model for an accurate prediction. Along with the variousmachine learning and data mining utilities applied to solar energy prediction, the processof feature selection is becoming an ultimate requirement for improving model buildingefficiency. In this paper, we consider the feature selection (FS) approach potential. Weprovide a detailed taxonomy of various feature selection techniques and examine theirusability and ability to deal with a solar energy forecasting problem, given meteorologicaland geographical data. We focus on filter-based, wrapper-based, and embedded-basedfeature selection methods. We use the reduced number of selected features, stability, andregression accuracy and compare feature selection techniques. Moreover, the experimentalresults demonstrate how the feature selection methods studied can considerably improvethe prediction process and how the selected features vary by method, depending on thegiven data constraints.