Genetic algorithms in feature and instance selection

Tsai, Chih‐Fong; Eberle, William; Chu, Chi-Yuan

doi:10.1016/j.knosys.2012.11.005

Cited by 212 publications

(84 citation statements)

References 53 publications

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“…Some papers proposed evolutionary optimization of feature and instance selection [14,18], but as already mentioned in section 2, we did not consider this option first because of the computational complexity and second because evolutionary optimization, similarly as feature construction methods (as PCA) does not enable us to understanding why particular results were obtained.…”

Section: Joined Feature and Instance Selection Before Network Learningmentioning

confidence: 99%

Data Selection for Neural Networks

Kordos

2017

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Abstract. Several approaches to joined feature and instance selection in neural network leaning are discussed and experimentally evaluated in respect to classification accuracy and dataset compression, considering also their computational complexity. These include various versions of feature and instance selection prior to the network learning, the selection embedded in the neural network and hybrid approaches, including solutions developed by us. The advantages and disadvantages of each approach are discussed and some possible improvements are proposed.

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Section: Joined Feature and Instance Selection Before Network Learningmentioning

confidence: 99%

Data Selection for Neural Networks

Kordos

2017

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“…Since trying to maintain the same accuracy as with the initial training set is difficult to fulfill in practical scenarios, much research has been recently devoted to enhance this process through the combination with other techniques. Some of these include Feature Selection [35], Ensemble methods [20] or modifications to the kNN rule [7].…”

Section: Background On Data Reductionmentioning

confidence: 99%

Prototype generation on structural data using dissimilarity space representation

Calvo-Zaragoza

Valero-Mas

Rico-Juan

2016

Neural Comput & Applic

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Data Reduction techniques play a key role in instance-based classification to lower the amount of data to be processed. Among the different existing approaches, Prototype Selection (PS) and Prototype Generation (PG) are the most representative ones. These two families differ in the way the reduced set is obtained from the initial one: while the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to delimit more efficiently decision boundaries, the operations required are not so well defined in scenarios involving structural data such as strings, trees or graphs. This work studies the possibility of using Dissimilarity Space (DS) methods as an intermediate process for mapping the initial structural representation to a statistical one, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that the proposed strategy is able to achieve significantly similar results to PS in the initial space, thus standing as a clear alternative to the classic approach, with some additional advantages derived from the DS representation.

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“…Genetic algorithms are used for feature selection and optimization of many algorithms [28], [29]. Genetic algorithms are widely used for many optimization needs.…”

Section: Genetic Algorithm Optimizationmentioning

confidence: 99%

Evolutionary k-nearest neighbor imputation algorithm for gene expression data

Silva¹,

Perera²

2017

Int J on Adv. in ICT for Emerging Countries

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Abstract-Large data sets are produced by the gene expression process which is done by using the DNA microarray technology. These gene expression data are recognized as a common data source which contains missing expression values. In this paper, we present a genetic algorithm optimized k-Nearest neighbor algorithm (Evolutionary kNNImputation) for missing data imputation. Despite the common imputation methods this paper addresses the effectiveness of using supervised learning algorithms for missing data imputation. Missing data imputation approaches can be categorized into four main categories and among the four approaches, our focus is mainly on local approach where the proposed Evolutionary k-Nearest Neighbor Imputation Algorithm falls in. The Evolutionary k-Nearest Neighbor Imputation Algorithm is an extension of the common knearest Neighbor Imputation Algorithm which the genetic algorithm is used to optimize some parameters of k-Nearest Neighbor Algorithm. The selection of similarity matrix and the selection of the parameter value k can be identified as the optimization problem. We have compared the proposed Evolutionary k-Nearest Neighbor Imputation algorithm with kNearest Neighbor Imputation algorithm and mean imputation method. The three algorithms were tested using gene expression datasets. Certain percentages of values are randomly deleted in the datasets and recovered the missing values using the three algorithms. Results show that Evolutionary kNNImputation outperforms kNNImputation and mean imputation while showing the importance of using a supervised learning algorithm in missing data estimation. Even though mean imputation happened to show low mean error for a very few missing rates, supervised learning algorithms became effective when it comes to higher missing rates in datasets which is the most common situation among datasets.

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Genetic algorithms in feature and instance selection

Cited by 212 publications

References 53 publications

Data Selection for Neural Networks

Data Selection for Neural Networks

Prototype generation on structural data using dissimilarity space representation

Evolutionary k-nearest neighbor imputation algorithm for gene expression data

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