Adaptive floating search methods in feature selection

Somol, Petr; Pudil, Pavel; Novovičová, Jana; Pacĺık, Pavel

doi:10.1016/s0167-8655(99)00083-5

Cited by 271 publications

(133 citation statements)

References 4 publications

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“…Our proposed algorithm has some similarities with adaptive floating search algorithm (Somol et al, 1999). However, our proposed algorithm does not require predefined user specified absolute generalization limit, adaptive calculation of absolute generalization limit, and termination criteria.…”

Section: Resultsmentioning

confidence: 99%

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On fuzzy-rough sets approach to feature selection

Bhatt

Gopal

2005

Pattern Recognition Letters

191

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Section: Resultsmentioning

confidence: 99%

“…However, our proposed algorithm does not require predefined user specified absolute generalization limit, adaptive calculation of absolute generalization limit, and termination criteria. Further, implementation of our proposed algorithm is very straight forward compared to ASFFS (Somol et al, 1999).…”

Section: Resultsmentioning

confidence: 99%

On fuzzy-rough sets approach to feature selection

Bhatt

Gopal

2005

Pattern Recognition Letters

191

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“…Thus, it is often preferable for many high dimensional problems to employ heuristic methods that compromise subset optimality for better computational efficiency. A few examples of such search strategies are sequential search [5,6], floating search [7][8][9], random mutation hill climbing [10] and evolutionary-based approaches [11][12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking

Senawi

Wei

Billings

2017

Pattern Recognition

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A substantial amount of datasets stored for various applications are often high dimensional with redundant and irrelevant features. Processing and analyzing data under such circumstances is time consuming and makes it difficult to obtain efficient predictive models. There is a strong need to carry out analyses for high dimensional data in some lower dimensions, and one approach to achieve this is through feature selection. This paper presents a new relevancy-redundancy approach, called the maximum relevance-minimum multicollinearity (MRmMC) method, for feature selection and ranking, which can overcome some shortcomings of existing criteria. In the proposed method,

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“…Under the first strategy, one starts with the full set of features and sequentially eliminates the weakest feature, while bottom-up methods first chooses the single best feature and iteratively add the best out of the remaining features. More advanced methods consider sets rather than single features at a time and combine forward and backward search with each other [27] [32].…”

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confidence: 99%

A Feature-Based Serial Approach to Classifier Combination

Bunke

Kandel

2002

Pattern Anal Appl

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Originality and ContributionThe main motivation behind existing methods of classifier combination is improvement of classification accuracy. In this paper we are concerned with a different problem, which is improvement of computational efficiency. Reducing computational effort is extremely important in data-intensive applications of pattern recognition such as clickstream analysis on the web. The classification rate of "lazy" learning methods, like k-NN Classifier, is especially sensitive to the dimensionality of the training set. The computational complexity of a k-NN Classifier depends on two main factors: the number of distance computations (proportional to the number of training examples considered for finding the nearest neighbor of a new instance) and the effort associated with calculating a distance to each training example (proportional to the number of features).Several algorithms have been suggested to reduce the number of distance computations. To the best of our knowledge, this work presents the first attempt to reduce the computational complexity To sum-up, this work is original in at least the following aspects: it uses a feature-based approach to reducing the computational burden of an instance-based classifier; it proposes a serial combination of two k-NN classifiers; and it presents a new feature selection method ("IFN + Relief"). Evaluation results demonstrate the broad practical implications of the proposed methodology.

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Adaptive floating search methods in feature selection

Cited by 271 publications

References 4 publications

On fuzzy-rough sets approach to feature selection

On fuzzy-rough sets approach to feature selection

A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking

A Feature-Based Serial Approach to Classifier Combination

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