Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

Yin, Ying; Zhao, Yuhai; Li, Chengguang; Zhang, Bin

doi:10.3390/app6060160

Cited by 7 publications

(5 citation statements)

References 41 publications

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“…Graph structure is a good data structure to represent the structural information of the research object, and it is also widely used in practical applications and research. 26 Graph-based learning can be extended to many fields, such as biopharmaceutical activity experiments, online product recommendations based on reviews, and classification of scientific publications. Similar to that, this work 27 java file is considered relevant to the software defect report, and vice versa.…”

Section: Multi-graph Learningmentioning

confidence: 99%

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Multi‐graph learning‐based software defect location

Yin

Shi

Zhao

et al. 2023

J Software Evolu Process

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Software quality is key to the success of software systems. Modern software systems are growing in their worth based on industry needs and becoming more complex, which inevitably increases the possibility of more defects in software systems. Software repairing is time‐consuming, especially locating the source files related to specific software defect reports. To locate defective source files more quickly and accurately, automated software defect location technology is generated and has a huge application value. The existing deep learning‐based software defect location method focuses on extracting the semantic correlation between the source file and the corresponding defect reports. However, the extensive code structure information contained in the source files is ignored. To this end, we propose a software defect location method, namely, multi‐graph learning‐based software defect location (MGSDL). By extracting the program dependency graphs for functions, each source file is converted into a graph bag containing multiple graphs (i.e., multi‐graph). Further, a multi‐graph learning method is proposed, which learns code structure information from multi‐graph to establish the semantic association between source files and software defect reports. Experiments' results on four publicly available datasets, AspectJ, Tomcat, Eclipse UI, and SWT, show that MGSDL improves on average 3.88%, 5.66%, 13.23%, 9.47%, and 3.26% over the competitive methods in five evaluation metrics, rank@10, rank@5, MRR, MAP, and AUC, respectively.

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Section: Multi-graph Learningmentioning

confidence: 99%

“…The k‐medoids algorithm 29 based on Hausdorff distance is usually used to degenerate multiple instances into a single instance. Hausdorff distance is a distance defined between any two sets in metric space, and it can also measure the similarity between two sets of point sets.…”

Section: Overall Flow Of Our Proposed Frameworkmentioning

confidence: 99%

Multi‐graph learning‐based software defect location

Yin

Shi

Zhao

et al. 2023

J Software Evolu Process

Self Cite

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“…In IoT robotics is the best example for ML-based algorithm, in which it learns from the previous task and executes the job in real-time [1]. Moreover, ML algorithms are used to forecast the event by learning from previous data, such as weather prediction, financial operations in banking, or tracking Covid patients on a location-based [2]. There are a different number of algorithms are available for numerous use cases, each algorithm has a distinct feature for a different category of data.…”

Section: Machine Learningmentioning

confidence: 99%

Towards Deep Learning: A Comprehensive Overview on PSO with Machine Learning

Sylla¹,

Coulibaly²,

Morizet³

2022

World Congress on Electrical Engineering and Computer Systems and Science

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In this era of deep learning where AI and machine learning models are used in well-known applications such as speech to text, real-time translation, image recognition, requires a large amount of data for the training. To process a large amount the data, model efficiency is a major concern, for this, there are few optimizations' methods being developed by a different researcher. In our research, we briefly discussed the famous optimization method such as PSO and their implementation with different machine learning models and perform experiments with different volumes of textual data and overview the performance with or without PSO algorithm, besides we also utilize a similar approach with neural networks. Experimental results show the proposed overview significance on data volume.

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“…The full name of MIML-ELM is Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine [ 24 ]. Extreme Learning Machine ( ELM ) is one of the models in Neural Networks and is extensively utilized in Single Hidden-layer Feed-forward Network .…”

Section: Related Workmentioning

confidence: 99%

Multi-Graph Multi-Label Learning Based on Entropy

Zhu

Zhao

2018

Entropy

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Abstract:Recently, Multi-Graph Learning was proposed as the extension of Multi-Instance Learning and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on Multi-Graph Multi-Label Learning, where each object is represented as a bag containing a number of graphs and each bag is marked with multiple class labels. It is an interesting problem existing in many applications, such as image classification, medicinal analysis and so on. In this paper, we propose an innovate algorithm to address the problem. Firstly, it uses more precise structures, multiple Graphs, instead of Instances to represent an image so that the classification accuracy could be improved. Then, it uses multiple labels as the output to eliminate the semantic ambiguity of the image. Furthermore, it calculates the entropy to mine the informative subgraphs instead of just mining the frequent subgraphs, which enables selecting the more accurate features for the classification. Lastly, since the current algorithms cannot directly deal with graph-structures, we degenerate

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Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

Cited by 7 publications

References 41 publications

Multi‐graph learning‐based software defect location

Multi‐graph learning‐based software defect location

Towards Deep Learning: A Comprehensive Overview on PSO with Machine Learning

Multi-Graph Multi-Label Learning Based on Entropy

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