Learning misclassification costs for imbalanced classification on gene expression data

Abstract Background Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem. However, the misclassification costs are usually determined empirically based on user expertise, which leads to unstable performance of cost-sensitive classification. Therefore, an efficient and accurate method is needed to calculate the optimal cost weights. Results In this paper, two approaches are proposed to search for the optimal cost weights, targeting at the highest weighted classification accuracy (WCA). One is the optimal cost weights grid searching and the other is the function fitting. Comparisons are made between these between the two algorithms above. In experiments, we classify imbalanced gene expression data using extreme learning machine to test the cost weights obtained by the two approaches. Conclusions Comprehensive experimental results show that the function fitting method is generally more efficient, which can well find the optimal cost weights with acceptable WCA.

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Applying Cost-Sensitive Extreme Learning Machine and Dissimilarity Integration to Gene Expression Data Classification

Computational Intelligence and Neuroscience ◽

10.1155/2016/8056253 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 12

Author(s):

Yanqiu Liu ◽

Huijuan Lu ◽

Ke Yan ◽

Haixia Xia ◽

Chunlin An

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Imbalanced Data ◽

Expression Data ◽

Classification Problems ◽

High Scale ◽

Rejection Cost ◽

Misclassification Costs ◽

Learning Machine ◽

The Cost

Embedding cost-sensitive factors into the classifiers increases the classification stability and reduces the classification costs for classifying high-scale, redundant, and imbalanced datasets, such as the gene expression data. In this study, we extend our previous work, that is, Dissimilar ELM (D-ELM), by introducing misclassification costs into the classifier. We name the proposed algorithm as the cost-sensitive D-ELM (CS-D-ELM). Furthermore, we embed rejection cost into the CS-D-ELM to increase the classification stability of the proposed algorithm. Experimental results show that the rejection cost embedded CS-D-ELM algorithm effectively reduces the average and overall cost of the classification process, while the classification accuracy still remains competitive. The proposed method can be extended to classification problems of other redundant and imbalanced data.

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Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification

International Journal of Data Mining and Bioinformatics ◽

10.1504/ijdmb.2015.069657 ◽

2015 ◽

Vol 12 (3) ◽

pp. 294 ◽

Cited By ~ 4

Author(s):

Huijuan Lu ◽

Shasha Wei ◽

Zili Zhou ◽

Yanzi Miao ◽

Yi Lu

Keyword(s):

Gene Expression ◽

Extreme Learning Machine ◽

Gene Expression Data ◽

Data Classification ◽

Expression Data ◽

Misclassification Cost ◽

Rejection Cost ◽

Learning Machine

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Sparse Representation for Classification of Tumors Using Gene Expression Data

Journal of Biomedicine and Biotechnology ◽

10.1155/2009/403689 ◽

2009 ◽

Vol 2009 ◽

pp. 1-6 ◽

Cited By ~ 45

Author(s):

Xiyi Hang ◽

Fang-Xiang Wu

Keyword(s):

Gene Expression ◽

Sparse Representation ◽

Gene Expression Data ◽

Least Square Method ◽

Classification Problem ◽

Least Square ◽

Support Vector ◽

Expression Data ◽

Classification Of Tumors

Personalized drug design requires the classification of cancer patients as accurate as possible. With advances in genome sequencing and microarray technology, a large amount of gene expression data has been and will continuously be produced from various cancerous patients. Such cancer-alerted gene expression data allows us to classify tumors at the genomewide level. However, cancer-alerted gene expression datasets typically have much more number of genes (features) than that of samples (patients), which imposes a challenge for classification of tumors. In this paper, a new method is proposed for cancer diagnosis using gene expression data by casting the classification problem as finding sparse representations of test samples with respect to training samples. The sparse representation is computed by thel1-regularized least square method. To investigate its performance, the proposed method is applied to six tumor gene expression datasets and compared with various support vector machine (SVM) methods. The experimental results have shown that the performance of the proposed method is comparable with or better than those of SVMs. In addition, the proposed method is more efficient than SVMs as it has no need of model selection.

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Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods

BMC Bioinformatics ◽

10.1186/s12859-019-2953-8 ◽

2019 ◽

Vol 20 (S9) ◽

Cited By ~ 1

Author(s):

Damiano Verda ◽

Stefano Parodi ◽

Enrico Ferrari ◽

Marco Muselli

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Cancer Diagnosis ◽

Cancer Biology ◽

Nearest Neighbor ◽

Support Vector ◽

Expression Data ◽

Network Support ◽

Supervised Methods ◽

Learning Machine

Abstract Background Logic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules. In this investigation the performance of LLM in classifying patients with cancer was evaluated using a set of eight publicly available gene expression databases for cancer diagnosis. LLM accuracy was assessed by summary ROC curve (sROC) analysis and estimated by the area under an sROC curve (sAUC). Its performance was compared in cross validation with that of standard supervised methods, namely: decision tree, artificial neural network, support vector machine (SVM) and k-nearest neighbor classifier. Results LLM showed an excellent accuracy (sAUC = 0.99, 95%CI: 0.98–1.0) and outperformed any other method except SVM. Conclusions LLM is a new powerful tool for the analysis of gene expression data for cancer diagnosis. Simple rules generated by LLM could contribute to a better understanding of cancer biology, potentially addressing therapeutic approaches.

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