Are More Features Better? A Response to<i>Attributes Reduction Using Fuzzy Rough Sets</i>

Jensen, Richard; Shen, Qiang

doi:10.1109/tfuzz.2009.2026639

Cited by 41 publications

(16 citation statements)

References 25 publications

(42 reference statements)

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“…Therefore, less storage capacity and lower computational cost were required in the proposed approach compared to the traditional condensing by clustering approach [53][54][55]. To assess the representative power of the extracted generalized exemplars from the employed dataset, unpaired t-tests were employed and achieved P-values are summarized in Table 12.…”

Section: Time-ordered Datasetsmentioning

confidence: 99%

A novel approach for extracting ideal exemplars by clustering for massivetime-ordered datasets

Ertuğrul¹

2017

Turk J Elec Eng & Comp Sci

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Abstract:The number and length of massive datasets have increased day by day and this yields more complex machine learning stages due to the high computational costs. To decrease the computational cost many methods were proposed in the literature such as data condensing, feature selection, and filtering. Although clustering methods are generally employed to divide samples into groups, another way of data condensing is by determining ideal exemplars (or prototypes), which can be used instead of the whole dataset. In this study, first the efficiency of traditional data condensing by clustering approach was confirmed according to obtained accuracies and condensing ratios in 9 different synthetic or real batch datasets. This approach was then improved to be employed in time-ordered datasets. In order to validate the proposed approach, 23 different real time-ordered datasets were used in experiments. Achieved mean RMSEs were 0.27 and 0.29 by employing the condensed (mean condensed ratio was 97.17%) and the whole datasets, respectively. Obtained results showed that higher accuracy rates and condensing ratios were achieved by the proposed approach.

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Section: Time-ordered Datasetsmentioning

confidence: 99%

A novel approach for extracting ideal exemplars by clustering for massivetime-ordered datasets

Ertuğrul¹

2017

Turk J Elec Eng & Comp Sci

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“…Just adding features to a model does not guarantee that it will get better. Additional features might represent redundant information, which would not translate into more accurate classifiers for certain machine learning models, or worse, they would contribute to the curse of dimensionality [25]. In order to make sure we are adding meaningful information we further analyzed our features.…”

Section: Measuring the Quality Of Sparcfire Featuresmentioning

confidence: 99%

Enhancing Automatic Prediction of Spirality using SpArcFiRe's Spiral Arm Analysis and Random Forests

Silva¹,

Cao²,

Hayes³

2018

Preprint

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Automated machine classifications of galaxies are necessary because the size of upcoming surveys will overwhelm human volunteers. We improve upon existing machine classification methods by adding the output of SpArcFiRe to the inputs of a machine learning model. We use the human classifications from Galaxy Zoo 1 (GZ1) to train a random forest of decision trees to reproduce the human vote distributions of the Spiral class. We prefer the random forest model over other black box models like neural networks because it allows us to trace post hoc the precise reasoning behind the classification of each galaxy. We find that, across a sample of 470,000 Sloan galaxies that are large enough that details could be seen if they were there, the combination of SpArcFiRe outputs with existing SDSS features provides a better machine classification than either one alone on comparison to Galaxy Zoo 1. We suggest that adding SpArcFiRe outputs as features to any machine learning algorithm will likely improve its performance.

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“…In addition, not all such features may be useful to perform classification [12], [15], [23], [25]. Due to measurement noise the use of extra features may even reduce the overall representational potential of the feature set and hence, the classification accuracy [16]. It is therefore, often necessary to employ a method that can determine the most significant features, based on sample measurements, to simplify the classification process, while ensuring high classification performance.…”

Section: Introductionmentioning

confidence: 99%

Support vector machine-based classification of rock texture images aided by efficient feature selection

Shang¹,

Barnes²

2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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Abstract-This paper presents a study on rock texture image classification using support vector machines (and also K-nearest neighbours and decision trees) with the aid of feature selection techniques. It offers both unsupervised and supervised methods for feature selection, based on data reliability and information gain ranking respectively. Following this approach, the conventional classifiers which are sensitive to the dimensionality of feature patterns, become effective on classification of images whose pattern representation may otherwise involve a large number of features. The work is successfully applied to complex images. Classifiers built using features selected by either of these methods generally outperform their counterparts that employ the full set of original features which has a dimensionality several folds higher than that of the selected feature subset. This is confirmed by systematic experimental investigations. This study therefore, helps to accomplish challenging image classification tasks effectively and efficiently. In particular, the approach retains the underlying semantics of a selected feature subset. This is very important to ensure that the classification results are understandable by the user.

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Are More Features Better? A Response toAttributes Reduction Using Fuzzy Rough Sets

Abstract: A recent TRANSACTIONS ON FUZZY SYSTEMS paper proposing a new fuzzy-rough feature selector (FRFS) has claimed that the more attributes remain in datasets, the better the approximations and hence resulting models. [Tsang et al., IEEE Trans.

Cited by 41 publications

References 25 publications

A novel approach for extracting ideal exemplars by clustering for massivetime-ordered datasets

A novel approach for extracting ideal exemplars by clustering for massivetime-ordered datasets

Enhancing Automatic Prediction of Spirality using SpArcFiRe's Spiral Arm Analysis and Random Forests

Support vector machine-based classification of rock texture images aided by efficient feature selection

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