Consistency-Based Feature Selection

Shin, Kilho; Xu, Xiaofang

doi:10.1007/978-3-642-04595-0_42

Cited by 20 publications

(25 citation statements)

References 5 publications

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“…Although the search space of INTERACT is very narrow, the combination of the SR function and the consistency measure keeps the accuracy performance good. LCC [4] improves INTERACT and can exhibit better accuracy. Although INTERACT and LCC are much faster than previous consistency-based algorithms described in the literature, they are not fast enough to apply to large datasets with thousands of instances and features.…”

Section: Problem Of Consistency Measuresmentioning

confidence: 99%

“…Feature selection has been intensively studied in machine learning research. The literature describes a class of feature selection algorithms that can solve this problem, referred to as consistency-based feature selection (for example, [1][2][3][4][5]). Figure 2 shows the result of feature selection using one of the consistency-based algorithms, namely, CWC (Combination of Weakest Components) [5].…”

Section: Introductionmentioning

confidence: 99%

“…Consistency-based algorithms require heavy computation and the amount of computation increases as the size of data increases so greatly as to make application to large data sets unfeasible. This paper's contribution is to improve the run-time performance of two consistency-based algorithms that are known the most accurate, namely CWC and LCC (Linear Consistency Constrained feature selection) [4]. We introduce two algorithms that perform well on big data: SCWC and SLCC.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, Algorithm 4 depicts the algorithm of LCC [4]. In contrast to CWC, LCC accepts a threshold parameter δ ≥ 0.…”

mentioning

confidence: 99%

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sCwc/sLcc: Highly Scalable Feature Selection Algorithms

et al. 2017

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Feature selection is a useful tool for identifying which features, or attributes, of a dataset cause or explain the phenomena that the dataset describes, and improving the efficiency and accuracy of learning algorithms for discovering such phenomena. Consequently, feature selection has been studied intensively in machine learning research. However, while feature selection algorithms that exhibit excellent accuracy have been developed, they are seldom used for analysis of high-dimensional data because high-dimensional data usually include too many instances and features, which make traditional feature selection algorithms inefficient. To eliminate this limitation, we tried to improve the run-time performance of two of the most accurate feature selection algorithms known in the literature. The result is two accurate and fast algorithms, namely SCWC and SLCC. Multiple experiments with real social media datasets have demonstrated that our algorithms improve the performance of their original algorithms remarkably. For example, we have two datasets, one with 15,568 instances and 15,741 features, and another with 200,569 instances and 99,672 features. SCWC performed feature selection on these datasets in 1.4 seconds and in 405 seconds, respectively. In addition, SLCC has turned out to be as fast as SCWC on average. This is a remarkable improvement because it is estimated that the original algorithms would need several hours to dozens of days to process the same datasets. In addition, we introduce a fast implementation of our algorithms: SCWC does not require any adjusting parameter, while SLCC requires a threshold parameter, which we can use to control the number of features that the algorithm selects.

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Section: Problem Of Consistency Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In addition, Algorithm 4 depicts the algorithm of LCC [4]. In contrast to CWC, LCC accepts a threshold parameter δ ≥ 0.…”

mentioning

confidence: 99%

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sCwc/sLcc: Highly Scalable Feature Selection Algorithms

et al. 2017

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“…This paper describes RBF neural network based on consistency evaluator for selection to find the best subset of features that not only maximizes the classification accuracy but minimizes the number of features. (10). In consistency-based feature selection, consistency measures are used to evaluate relevance of feature subsets.…”

Section: Introductionmentioning

confidence: 99%

Radial Basis Function (RBF) Neural Network Classification based on Consistency Evaluation Measure

MyaThandar¹,

Khine²

2012

IJCA

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Many researchers have been applied artificial neural networks in clinical diagnosis, image analysis, signal analysis, interpretation and various classification problems. Among artificial neural networks, RBF neural network has a single hidden layer and it is used to classify complex problems, whereas an MLP may have one or more hidden layers. Many feature selection methods have become important preprocessing steps to improve training performance and accuracy before classification. Consistency-based feature selection is an important category of feature selection research. This paper presents about RBF neural network classification based on consistency measure for medical datasets. There are irrelevant features in medical dataset and it becomes easier to train RBF network by removing unnecessary features. Therefore, this paper shows higher accuracy, better network performance and less time complexity by using RBF classifier based on consistency based feature selection.

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