An Innovative Feature Selection Using Fuzzy Entropy

Parvïn, Hamïd; Minaei-Bidgoli, Behrouz; Ghaffarian, Hossein

doi:10.1007/978-3-642-21111-9_65

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…Nowadays, usage of recognition systems has found many applications in almost all fields [16][17][18][19][20][21][22][23][24][25][26][27][28]. There are many inherently different classifiers in the pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

A Heuristically Perturbation of Dataset to Achieve a Diverse Ensemble of Classifiers

Parvïn

Parvin

Rezaei

et al. 2012

Lecture Notes in Computer Science

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Ensemble methods like Bagging and Boosting which combine the decisions of multiple hypotheses are among the strongest existing machine learning methods. The diversity of the members of an ensemble is known to be an important factor in determining its generalization error. We present a new method for generating ensembles, named CDEBMTE (Creation of Diverse Ensemble Based on Manipulation of Training Examples), that directly constructs diverse hypotheses using manipulation of training examples in three ways: (1) sub-sampling training examples, (2) decreasing/increasing errorprone training examples and (3) decreasing/increasing neighbor samples of error-prone training examples.The technique is a simple, general meta-learner that can use any strong learner as a base classifier to build diverse committees. Experimental results using two well-known classifiers (1) decision-tree induction and (2) multilayer perceptron as two base learners demonstrate that this approach consistently achieves higher predictive accuracy than both the base classifier, Adaboost and Bagging. CDEBMTE also outperforms Adaboost more prominent when training data size is becomes larger.We propose to show that CDEBMTE can be effectively used to achieve higher accuracy and to obtain better class membership probability estimates.Experimental results using two well-known classifiers as two base learners demonstrate that this approach consistently achieves higher predictive accuracy than both the base classifier, Adaboost and Bagging. CDEBMTE also outperforms Adaboost more prominent when training data size is becomes larger.

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Section: Introductionmentioning

confidence: 99%

A Heuristically Perturbation of Dataset to Achieve a Diverse Ensemble of Classifiers

Parvïn

Parvin

Rezaei

et al. 2012

Lecture Notes in Computer Science

Self Cite

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“…This feature selection method selects relevant features to get higher average classification accuracy. A method for innovative feature subset selection based on Fuzzy entropy measure was proposed by BehrouzMinaciet al [21] . S.Sethuramalingam et al [22] has presented a method on hybrid feature selection for network intrusion.…”

Section: Related Workmentioning

confidence: 99%

Optimal Feature Selection Algorithm for High Dimensional Data Sets Using Particle Swarm Optimization

2017

IJLTET

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“…Nowadays, usage of recognition systems has found many applications in almost all fields [23][24][25][26][27][28][29][30][31][32][33][34][35]. K-Nearest Neighbor (kNN) classifier is one of the most fundamental recognition systems.…”

Section: Introductionmentioning

confidence: 99%

Text Recognition with k-means Clustering

Jamnejad¹,

Heidarzadegan²,

Meshki³

2014

RCS

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A thesaurus is a reference work that lists words grouped together according to similarity of meaning (containing synonyms and sometimes antonyms), in contrast to a dictionary, which contains definitions and pronunciations. This paper proposes an innovative approach to improve the classification performance of Persian texts considering a very large thesaurus. The paper proposes a flexible method to recognize and categorize the Persian texts employing a thesaurus as a helpful knowledge. In the corpus, when utilizing the thesaurus the method obtains a more representative set of wordfrequencies comparing to those obtained when the method disables the thesaurus. Two types of word relationships are considered in our used thesaurus. This is the first attempt to use a Persian thesaurus in the field of Persian information retrieval. The k-nearest neighbor classifier, decision tree classifier and k-means clustering algorithm are employed as classifier over the frequency based features. Experimental results indicate enabling thesaurus causes the method significantly outperforms in text classification and clustering.

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An Innovative Feature Selection Using Fuzzy Entropy

Cited by 10 publications

References 11 publications

A Heuristically Perturbation of Dataset to Achieve a Diverse Ensemble of Classifiers

A Heuristically Perturbation of Dataset to Achieve a Diverse Ensemble of Classifiers

Optimal Feature Selection Algorithm for High Dimensional Data Sets Using Particle Swarm Optimization

Text Recognition with k-means Clustering

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