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A Benchmark of Prevalent Feature Selection Algorithms on a Diverse Set of Classification Problems
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Feature selection is the process of automatically selecting important features from data. It is an essential part of machine learning, artificial intelligence, data mining, and modelling in general. There are many feature selection algorithms available and the appropriate choice can be difficult. The aim of this thesis was to compare feature selection algorithms in order to provide an experimental basis for which algorithm to choose. The first phase involved assessing which algorithms are most common in the scientific community, through a systematic literature study in the two largest reference databases: Scopus and Web of Science. The second phase involved constructing and implementing a benchmark pipeline to compare 31 algorithms’ performance on 50 data sets.The selected features were used to construct classification models and their predictive performances were compared, as well as the runtime of the selection process. The results show a small overall superiority of embedded type algorithms, especially types that involve Decision Trees. However, there is no algorithm that is significantly superior in every case. The pipeline and data from the experiments can be used by practitioners in determining which algorithms to apply to their respective problems.

Abstract [sv]

Variabelselektion är en process där relevanta variabler automatiskt selekteras i data. Det är en essentiell del av maskininlärning, artificiell intelligens, datautvinning och modellering i allmänhet. Den stora mängden variabelselektionsalgoritmer kan göra det svårt att avgöra vilken algoritm som ska användas. Målet med detta examensarbete är att jämföra variabelselektionsalgoritmer för att ge en experimentell bas för valet av algoritm. I första fasen avgjordes vilka algoritmer som är mest förekommande i vetenskapen, via en systematisk litteraturstudie i de två största referensdatabaserna: Scopus och Web of Science. Den andra fasen bestod av att konstruera och implementera en experimentell mjukvara för att jämföra algoritmernas prestanda på 50 data set. De valda variablerna användes för att konstruera klassificeringsmodeller vars prediktiva prestanda, samt selektionsprocessens körningstid, jämfördes. Resultatet visar att inbäddade algoritmer i viss grad är överlägsna, framför allt typer som bygger på beslutsträd. Det finns dock ingen algoritm som är signifikant överlägsen i varje sammanhang. Programmet och datan från experimenten kan användas av utövare för att avgöra vilken algoritm som bör appliceras på deras respektive problem.

Place, publisher, year, edition, pages
2018. , p. 55
Series
TRITA-CBH-GRU ; 2018:32
Keywords [en]
feature selection, variable selection, attribute selection, machine learning, data mining, benchmark, classification
Keywords [sv]
variabelselektion, maskininlärning, datautvinning, klassificering
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-228614OAI: oai:DiVA.org:kth-228614DiVA, id: diva2:1212104
External cooperation
Nordron AB
Subject / course
Medical Engineering
Educational program
Master of Science in Engineering - Medical Engineering
Supervisors
Examiners
Available from: 2018-06-25 Created: 2018-06-01 Last updated: 2018-06-25Bibliographically approved

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