Comparison of hybrid feature selection models on gene expression data

Saengsiri, Patharawut; Meesad, Phayung; Wichian, Sageemas Na; Unger, Herwig

doi:10.1109/ictke.2010.5692905

Cited by 20 publications

(12 citation statements)

References 12 publications

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“…Most of the genes are not related to cancer. The challenge on microarray data is feature selection that searches for a subset of genes that are responsible for the cause of cancer [21]. This paper proposes a two-step feature extraction method.…”

Section: Proposed Two Step Feature Extraction Methodsmentioning

confidence: 99%

Two Step Feature Extraction Method for Microarray Cancer Data using Support Vector Machines

Arunkumar¹,

Ramakrishnan²

2014

IJCA

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Diagnosis of cancer is one of the most emerging clinical applications in microarray gene expression data. However, cancer classification on microarray gene expression data still remains a difficult problem. The main reason for this is the significantly large number of genes present relatively compared to the number of available training samples. In this paper, a novel approach to feature extraction combining the statistical t-test and absolute scoring is proposed for achieving better classification rate. Suitable classification approaches using the linear Support Vector Machines, the Proximal Support Vector Machines and the Newton Support Vector Machines is also discussed. A comparative analysis on the different techniques for feature extraction is also presented. Microarray cancer data based on Adenoma and Carcinoma with 7086 and 7457 genes of 4 and 18 patients respectively is used for this study. Increase in the classification rate of the proposed new method is clearly demonstrated in the results. General TermsNeural Networks, Support Vector Machines. KeywordsNormalization, Linear SVM, Proximal SVM, Newton SVM, absolute scoring PROPOSED METHODThis paper proposes a new method in feature extraction that combines the t-test statistics and the absolute scoring method. This proposed method produces higher accuracy in the classification rate when linear, proximal or Newton SVM is used for classification. The steps involved are given as under:

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Section: Proposed Two Step Feature Extraction Methodsmentioning

confidence: 99%

Two Step Feature Extraction Method for Microarray Cancer Data using Support Vector Machines

Arunkumar¹,

Ramakrishnan²

2014

IJCA

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“…Most common selection methods are Chi square and document frequency, information gain and mutual information. [9] [10] We applied Pearson's Chi-squared to our dataset to select the suitable features by observing if the occurrence of a specific feature is independent of the class. After selecting the most important features, each document is represented as a weighted vector based on the words found in the text.…”

Section: Feature Selectionmentioning

confidence: 99%

Multi-Class Text Supervised Classification on Romanian Financial Banking Reviews

Raicu¹,

Bologa²,

Constantinescu³

2019

Proceedings of the 18th International Conference on INFORMATICS in ECONOMY Education, Research and Business Technologies

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Nowadays, with the proliferation of social networks, forum discussions, blogs, and other forms of expression, more and more users have engaged in financial banking communities to gather information about financial banking products or services, to share experiences or to interact with other users. Reviews expressed by users on social media and Internet search activity are a valuable source of information. Text Classification is a growing research area used in social media analysis. The usefulness of Text Classification is manifested in different areas for various needs. For instance, the ability to automatically classify a review into its right financial banking product or service category would be appreciated by users when looking for a specific product or service as well as financial banking institutions to analyze the customers' feedback. Most of the research papers are focusing on exploring Text Classification methods for English-based text. Further, supervised machine learning methods for Text Classification require a huge data volume. In this paper, we focus on implementing supervised machine learning methods for multi-label text classification of Romanian financial banking reviews. We investigate the efficiency of using various supervised machine learning algorithms such as Random Forest, Support Vector Machine, Multinomial Naïve Bayes and Logistic Regression to classify text applied to a limited amount of Romanian financial banking reviews. We analyze the corpus, features and the results data. The baseline features are unigrams and bigrams weighted by TF-IDF. The results illustrate that the SVM classifier outperforms in classification of text into 30 categories of financial banking products and services from Romanian reviews.

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“…Chi Square is a statistical theory that can be used to discover the correlation relation between two attributes, it helps in identifying if two variables are related to each other. The term `chi square' (pronounced with a hard `ch'): is come from the Greek letter which is used to define the distribution of the variables, (also known as the Pearson 2 statistic) [12] [14]. It will be seen that the elements on which this distribution is based are squared, so that the symbol 2 is used to denote the distribution.…”

Section: Chi-square Testmentioning

confidence: 99%

Analysis the Relationship between Social Media and Education System in Kurdistan region of Iraq Using Chi-Square Test

Rassool

2018

ACAD J NAWROZ UNIV

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At the beginning of 21st century, the world has faced many challenges and rapid change in the educational environments. The globalization and the birth of internet lead the appearance of social media. The new education system has to cope with the above developments and to realize the demand of the new technologies and their effect on the students learning life. To analysis the effect of social network on education, the Chi-Square test was used to indicate the association relation between the Social Media tools and education. I have used real data of students from two University the first is Government (University Of Duhok) and the other is private-University (Nawroz University) at Duhok-Kurdistan region of Iraq. This paper indicates the association and correlation between the education and the Social Media, and conclude that the more than 85% of students used Social Media for communication and only approximate 45% used it for learning and teaching, also this paper indicates a need to orientate and guidance the education system in Kurdistan towered of using the social media in education for elevation of education quality and enhancing their learning because social media facilitate the learning system by participating the educations knowledge in different places of world.

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Comparison of hybrid feature selection models on gene expression data

Cited by 20 publications

References 12 publications

Two Step Feature Extraction Method for Microarray Cancer Data using Support Vector Machines

Two Step Feature Extraction Method for Microarray Cancer Data using Support Vector Machines

Multi-Class Text Supervised Classification on Romanian Financial Banking Reviews

Analysis the Relationship between Social Media and Education System in Kurdistan region of Iraq Using Chi-Square Test

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