Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking

Bermejo, Pablo; Ossa, Luis de la; Gámez, José A.; Puerta, José M.

doi:10.1016/j.knosys.2011.01.015

Cited by 143 publications

(60 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For feature selection, we used the correlation-based feature selector (CFS) with different search algorithms: re-ranking search algorithm [13], best first search algorithm, particle swarm optimization (PSO) search algorithm [14,15], and tabu search algorithm [16,17]. We also used the ranker search method with different attribute evaluators: Pearson's correlation, chi-squared distribution, information gain, and gain ratio.…”

Section: Feature Selection and Classification Methodsmentioning

confidence: 99%

Positive Impression of Low-Ranking Microrn as in Human Cancer Classification

Piao

et al. 2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

View full text Add to dashboard Cite

show abstract

Section: Feature Selection and Classification Methodsmentioning

confidence: 99%

Positive Impression of Low-Ranking Microrn as in Human Cancer Classification

Piao

et al. 2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

View full text Add to dashboard Cite

show abstract

“…It becomes important to the success of the tasks that apply machine learning approach especially when the data have many irrelevant or redundant features. In general, the features selection algorithms can be categorized as wrapper approach and filter approach [34] [1].…”

Section: Feature Rankingmentioning

confidence: 99%

Spectral-based Features Ranking for Gamelan Instruments Identification using Filter Techniques

2013

View full text Add to dashboard Cite

Abstrak Kata kunci: support vector machine, transkripsi otomatis, Gain Ratio, ekstraksi fitur Abstract In this paper, we describe an approach of spectral-based features ranking for Javanese gamelan instruments identification using filter techniques. The model extracted spectral-based features set of the signal using Short Time Fourier Transform (STFT). The rank of the features was determined using the five algorithms; namely ReliefF, Chi-Squared, Information Gain, Gain Ratio, and Symmetric Uncertainty. Then, we tested the ranked features by cross validation using Support Vector Machine (SVM). The experiment showed that Gain Ratio algorithm gave the best result, it yielded accuracy of 98.93%.

show abstract

“…Such a division is similar to those approaches in feature selection. Previous studies have validated that ranking-based feature ordering approaches are better than the contribution-based ones usually at least in two different aspects: time [8] and error rate [9]. Different from feature selection, which attempts to search a feature subset or reduce feature weights for the optimal results, feature ordering aims to sort features for IAL purpose by some criteria.…”

Section: Feature Ordering In Ialmentioning

confidence: 99%

Evolving Linear Discriminant in a Continuously Growing Dimensional Space for Incremental Attribute Learning

Wang

Guan

Ting

et al. 2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Feature Ordering is a unique preprocessing step in Incremental Attribute Learning (IAL), where features are gradually trained one after another. In previous studies, feature ordering derived based upon each individual feature's contribution is time-consuming. This study attempts to develop an efficient feature ordering algorithm by some evolutionary approaches. The feature ordering algorithm presented in this paper is based on a criterion of maximum mean of feature discriminability. Experimental results derived by ITID, a neural IAL algorithm, show that such a feature ordering algorithm has a higher probability to obtain the lowest classification error rate with datasets from UCI Machine Learning Repository.

show abstract

Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking

Cited by 143 publications

References 23 publications

Positive Impression of Low-Ranking Microrn as in Human Cancer Classification

Positive Impression of Low-Ranking Microrn as in Human Cancer Classification

Spectral-based Features Ranking for Gamelan Instruments Identification using Filter Techniques

Evolving Linear Discriminant in a Continuously Growing Dimensional Space for Incremental Attribute Learning

Contact Info

Product

Resources

About