Extreme learning machine (ELM) techniques have received considerable attention in the computational intelligence and machine learning communities because of the significantly low computational time required for training new classifiers. ELM provides solutions for regression, clustering, binary classification, multiclass classifications and so on, but not for multi-label learning. Multi-label learning deals with objects having multiple labels simultaneously, which widely exist in real-world applications. Therefore, a thresholding method-based ELM is proposed in this paper to adapt ELM to multi-label classification, called extreme learning machine for multi-label classification (ELM-ML). ELM-ML outperforms other multi-label classification methods in several standard data sets in most cases, especially for applications which only have a small labeled data set.

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Abstract. Since the introduction of DNA technology into the field of forensic science, it has gradually become the main means of forensic evidence identification in criminal cases. The forensic DNA evidence produced by this technology has important functions in criminal proceedings, which can provide clues for the investigation and provide the basis for the identification of criminals. However, because of the limitations of the scientific technology, DNA evidence in the criminal justice function has not been fully played, there are still many problems in the application. Therefore, in the criminal procedure to standardize the collection and detection of DNA evidence, improve the DNA evidence in court review procedures, in order to better play its role in the court trial.