A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm

Aci, Mehmet; Inan, Cigdem; Avcı, Mutlu

doi:10.1016/j.eswa.2009.12.004

Cited by 64 publications

(38 citation statements)

References 14 publications

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“…Usually after some trials a k value which perform finest is preferred. In our work, experimentation's are implemented on several values of k, and found that at k=7 the classifier provides the best result.The Algorithmic description of KNN is given in [19].…”

Section: K Nearest Neighbor (Knn)mentioning

confidence: 99%

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CLOM: Counting label occurrence matrix for feature extraction in MR images

Nagpal

Vidyarthi

Mittal

2015

2015 International Conference on Signal Processing and Communication (ICSC)

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The general phenomenon for Image Classification is based on the Feature extraction mechanism. In every domain of image analysis, the classification accuracy is dependent on how better the feature set is generated which helps the machine to learn and predict the unknown sample class label. In this paper, a novel feature extraction mechanism is proposed and named as Counting Label Occurrence Matrix (CLOM). CLOM is based on the counting label of the gray level intensity values of an image and used to extract the textural features of an image for image classification. Four different orientations are used in CLOM for extracting features based on anticipated algorithm. Proposed feature extraction mechanism is dynamic in nature and is used in any domain of image processing and machine learning. CLOM algorithm is compared with some feature extraction algorithms like GLCM (gray level co-occurrence matrix) and Run length Matrix when CLOM is tested in medical domain for classification of brain tumor images. The experimental result shows that the proposed algorithm gives better accuracy than past algorithms when tested with classifiers like KNN and BPNN.

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Section: K Nearest Neighbor (Knn)mentioning

confidence: 99%

“…The performance of a classifier depends on the number of present training samples. Here two supervised learning classifier K-Nearest Neighbor (KNN) [8,19] and Back Propagation Neural Network (BPNN) [20] are used for the classification of input feature vector.…”

Section: Classificationmentioning

confidence: 99%

CLOM: Counting label occurrence matrix for feature extraction in MR images

Nagpal

Vidyarthi

Mittal

2015

2015 International Conference on Signal Processing and Communication (ICSC)

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“…In order to link these daily, weekly, and monthly EWIGIIs into b-EWIGII, genetic algorithm (GA) was chosen as proper EA [1,6]. Before GA optimization, small preliminary experiments were performed to obtain a proper parameter setting for a successful implementation of GA.…”

Section: Phase 1: Oracle Classifier Constructionmentioning

confidence: 99%

Bayesian forecaster using class-based optimization

Ahn

Byun

et al. 2011

Appl Intell

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Suppose that several forecasters exist for the problem in which class-wise accuracies of forecasting classifiers are important. For such a case, we propose to use a new Bayesian approach for deriving one unique forecaster out of the existing forecasters. Our Bayesian approach links the existing forecasting classifiers via class-based optimization by the aid of an evolutionary algorithm (EA). To show the usefulness of our Bayesian approach in practical situations, we have considered the case of the Korean stock market, where numerous lag-l forecasting classifiers exist for monitoring its status.

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“…However, literature (Miao et al, 2009;Aci et al, 2010;Li et al, 2011b;Meesad et al, 2011) shows that the combination of different classification algorithms (hybrid approach) provides better results and increased text categorization performance instead of applying a single pure method. The result of applying hybrid approach to large text corpora heavily depends on the test data sets.…”

Section: Hybrid Approachmentioning

confidence: 99%

“…The main purpose of the comparison of hybrid approach is to highlight the applicability of different classification algorithms and complement their limitations (Aci et al, 2010).…”

Section: Hybrid Approachmentioning

confidence: 99%

A survey on text mining in social networks

Irfan¹,

King²,

Grages³

et al. 2015

The Knowledge Engineering Review

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In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.

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A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm

Cited by 64 publications

References 14 publications

CLOM: Counting label occurrence matrix for feature extraction in MR images

CLOM: Counting label occurrence matrix for feature extraction in MR images

Bayesian forecaster using class-based optimization

A survey on text mining in social networks

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