An improvement on floating search algorithms for feature subset selection

Nakariyakul, Songyot; Casasent, David P.

doi:10.1016/j.patcog.2008.11.018

Cited by 119 publications

(65 citation statements)

References 14 publications

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“…* Forward methods: starting with an empty set of features, they add one feature at each step until getting the optimal subset of features [195] [185]. * Backward methods: starting from a set containing all the features, they discard one feature at each step until getting the optimal set of features [165] [207] [210]. * Random methods: they can try several subsets of features in a random way, trying to avoid to be trapped in local optima (i.e.…”

Section: Feature Selectionmentioning

confidence: 99%

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Gibert

Sànchez–Marrè

Izquierdo

2016

AIC

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One of the important issues related with all types of data analysis, either statistical data analysis, machine learning, data mining, data science or whatever form of data-driven modeling, is data quality. The more complex the reality to be analyzed is, the higher the risk of getting low quality data. Unfortunately real data often contain noise, uncertainty, errors, redundancies or even irrelevant information. Useless models will be obtained when built over incorrect or incomplete data. As a consequence, the quality of decisions made over these models, also depends on data quality. This is why pre-processing is one of the most critical steps of data analysis in any of its forms. However, pre-processing has not been properly systematized yet, and little research is focused on this. In this paper a survey on most popular pre-processing steps required in environmental data analysis is presented, together with a proposal to systematize it. Rather than providing technical details on specific pre-processing techniques, the paper focus on providing general ideas to a non-expert user, who, after reading them, can decide which one is the more suitable technique required to solve his/her problem.

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Section: Feature Selectionmentioning

confidence: 99%

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Gibert

Sànchez–Marrè

Izquierdo

2016

AIC

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“…The benefit of ASFFS is that it provides a less redundant subset than the SFFS algorithm. Nakariyakul and Casasent [14] came up with an improved forward floating search algorithm, which has a new search step to check whether to replace a weak feature and remove it again until the replacement can no longer improve the criterion function. They found that this method obtained optimal solutions for many feature subsets and was less computationally intensive than exhaustive search optimal feature selection algorithms.…”

Section: Floating Search Methodsmentioning

confidence: 99%

Improving Floating Search Feature Selection using Genetic Algorithm

Homsapaya

Sornil

2017

J. ICT Res. Appl.

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Abstract. Classification, a process for predicting the class of a given input data, is one of the most fundamental tasks in data mining. Classification performance is negatively affected by noisy data and therefore selecting features relevant to the problem is a critical step in classification, especially when applied to large datasets. In this article, a novel filter-based floating search technique for feature selection to select an optimal set of features for classification purposes is proposed. A genetic algorithm is employed to improve the quality of the features selected by the floating search method in each iteration. A criterion function is applied to select relevant and high-quality features that can improve classification accuracy. The proposed method was evaluated using 20 standard machine learning datasets of various size and complexity. The results show that the proposed method is effective in general across different classifiers and performs well in comparison with recently reported techniques. In addition, the application of the proposed method with support vector machine provides the best performance among the classifiers studied and outperformed previous researches with the majority of data sets.

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“…Hence, wrapper approaches are frequently used [20] which select the features based on the performance of classification algorithm. However, E. Tuv, et al report that, due to the procedural complexity involved in both filters and wrapper, Feature Ensembles can be an alternate strategy in feature subsetting [21].As advancements to the aforesaid work, few research works are carried out using heuristic approaches like Sequential Floating Forward Selection, Sequential Floating Backward Elimination, Genetic Algorithm [23,24,25,26] and meta heuristic approach [27] for selecting the features. It is also observed that when no additional information is provided for the feature selection, then Rough Set Theory can also play a significant role in determining dispensable features [38,39].…”

Section: Fig 1 Various Existing Feature Selection Strategiesmentioning

confidence: 99%

Target Class Supervised Feature Subsetting

Nagabhushan¹,

Meenakshi²

2014

IJCA

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Dimensionality Reduction may result in contradicting effectsthe advantage of minimizing the number of features coupled with the disadvantage of information loss leading to incorrect classification or clustering. This could be the problem when one tries to extract all classes present in a high dimensional population. However in real life, it is often observed that one would not be interested in looking into all classes present in a high dimensional space but one would focus on one or two or few classes at any given instant depending upon the purpose for which data is analyzed. The proposal in this research work is to make the dimensionality reduction more effective, whenever one is interested specifically in a target class, not only in terms of minimizing the number of features, also in terms of enhancing the accuracy of classification particularly with reference to the target class. The objective of this research work hence is to realize effective feature subsetting supervised by the specified target class. A multistage algorithm is proposed-in the first stage least desired features which do not contribute substantial information to extract the target class are eliminated, in the second stage redundant features are identified and are removed to overcome redundancy, and in the final stage more optimal set of features are derived from the resultant subset of features. Suitable computational procedures are devised and reduced feature sets at different stages are subjected for validation. Performance is analysed through extensive experiments. The multistage procedure is also tested on a hyperspectral AVIRIS Indiana Pine data set.

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An improvement on floating search algorithms for feature subset selection

Cited by 119 publications

References 14 publications

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Improving Floating Search Feature Selection using Genetic Algorithm

Target Class Supervised Feature Subsetting

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