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Improving ELM-based microarray data classification by diversified sequence features selection

  • Extreme Learning Machine and Applications
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Abstract

In this paper, we focus on the problem of extreme learning machine (ELM)-based microarray data classification. Different from the traditional classification problem, the goal in this case is not just to predict the class labels for the unseen samples, but to make clear what lead to the results, i.e., the genes involving with a specific disease. This is especially significant for biologists, since they need to decipher the causes of disease. As a black-box method, ELM could not measure up to the task by itself. In this work, we propose a diversified sequence feature selection-based framework to address the problem. In this framework, (1) a sequence model, EWave, is introduced to ensure the structural ordering information among genes exploitable; (2) a concept of irreducible sequence is proposed, where the genes work as an orderly whole to keep high confidence with a specific class and any reduction in the genes decreases the confidence much. An efficient sequence mining algorithm together with some effective pruning rules is developed to mine such sequences; and (3) we study how to extract a set of diversified sequence features as the representative of all mined results. The problem is proved to be NP-hard. A greedy algorithm is presented to approximate the optimal solution. Experimental results show that the proposed approach significantly improves the efficiency and the effectiveness of ELM w.r.t some widely used feature selection techniques.

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Notes

  1. That is, IG (information gain), TR (twoing rule), SM (sum minority), MM (max minority), GI (Gini index) and SV (sum of variance).

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Acknowledgments

Supported by \(863\) program \((2012\hbox {AA}011004), \,973\) program \((2011\hbox {CB}302200\hbox {-G})\), National Science Fund for Distinguished Young Scholars \((61025007)\), State Key Program of National Natural Science of China \((60933001,\,61332014)\), National Natural Science Foundation of China \((61272182,\,61100028,\,61073063,\,61173030)\), New Century Excellent Talents (NCET-\(11\)-\(0085\)), China Postdoctoral Science Foundation \((2012\hbox {T}50263,\,2011\hbox {M}500568)\), and Fundamental Research Funds for the Central Universities \((\hbox {N}110404005,\,\hbox {N}110404017)\).

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Authors and Affiliations

  1. College of Information Science and Engineer, Northeastern University, Shenyang, 110819, China

    Yuhai Zhao, Guoren Wang, Ying Yin, Yuan Li & Zhanghui Wang

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  1. Yuhai Zhao
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  2. Guoren Wang
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  3. Ying Yin
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  4. Yuan Li
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  5. Zhanghui Wang
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Correspondence to Yuhai Zhao.

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Zhao, Y., Wang, G., Yin, Y. et al. Improving ELM-based microarray data classification by diversified sequence features selection. Neural Comput & Applic 27, 155–166 (2016). https://doi.org/10.1007/s00521-014-1571-7

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