Semi-supervised Machine Learning Algorithm Research Articles

A Pattern Query Strategy Based on Semi-supervised Machine Learning in Distributed WSNs

Hardware of Distributed WSNs is limited, which turns into a big challenge. In recent years, there are some pattern-based query strategies have been proposed. But compared to the methods based on machine learning, these methods need high energy consumption in the calculation of distance. In this paper, we propose a pattern-based query strategy based on semi-supervised machine learning in wireless sensor network. The strategy uses historical time series data streams for o†ine machine learning. First of all, we flnd out the flxed length subsequences and screen out the suitable data sequences as training dataset and sample dataset. And then, because labeled data is less in practical application, we do label propagation with graph-based semi-supervised machine learning algorithm, Local and Global Consistency (LGC), to increase the number of labeled data sequences. Next, we use the supervised learning algorithm Support Vector Machine (SVM) to train a classifler. Finally, for a new data sequence, we can directly get its category through the classifler. Experimental results show that the performance of our strategy is almost better than other pattern-based query strategies on classiflcation accuracy and energy consumption.

Semi-Supervised Machine Learning Algorithms for Classifying the Imbalanced Protocol Flows

The collision that between a car and a train is the main type of accidents in the case railway level-crossing, which is proved by the arrangement diagram analysing. The fault tree analysis and the event tree analysis are used to assess the level of the risk of the level-crossing quantificationally. Some conclusions can be drawn: the collision of the railway level-crossing that between a car and a train will happen 2.552 in a year, which can bring 0.061 equivalent fatalities. This paper puts forward some precautionary measures that based on the minimal cut set of the collision of the accident and the most probable or the highest risk event.

Автоматическое извлечение терминов из коллекции текстов предметной области с помощью Википедии

Automatic term acquisition is an important task for many applications related to domain-specific texts processing. At present there are many methods for automatic term acquisition, but they are highly dependent on language and domain of input text collection. Also these methods, in general, use domain-specific text collection only, while many external resources are underutilized. We argue that one of the most promising external resources for automatic term acquisition is the online encyclopedia Wikipedia. In this paper we propose two new features: Hyperlink probability - normalized frequency showing how often the candidate terms is a hyperlink in Wikipedia articles; and Semantic relatedness to the domain key - arithmetic mean of semantic relatedness to the key concepts of a given domain; those key concepts are determined automatically on the basis of input domain-specific text collection. In addition, we propose a new method for automatic term acquisition. It is based on semi-supervised machine learning algorithm, but it does not require labeled data. Outline of the method is to extract the best 100-300 candidates presented in Wikipedia by using a special method for term acquisition, and then to use these candidates as positive examples to construct a model for a classifier based on positive-unlabeled learning algorithm. An experimental evaluation conducted for the four domains (board games, biomedicine, computer science, agriculture) shows that the proposed method significantly outperforms existed one and is domain-independent: the average precision is higher by 5-17% than that of the best method for a particular data set.

A cross-validation scheme for machine learning algorithms in shotgun proteomics

Peptides are routinely identified from mass spectrometry-based proteomics experiments by matching observed spectra to peptides derived from protein databases. The error rates of these identifications can be estimated by target-decoy analysis, which involves matching spectra to shuffled or reversed peptides. Besides estimating error rates, decoy searches can be used by semi-supervised machine learning algorithms to increase the number of confidently identified peptides. As for all machine learning algorithms, however, the results must be validated to avoid issues such as overfitting or biased learning, which would produce unreliable peptide identifications. Here, we discuss how the target-decoy method is employed in machine learning for shotgun proteomics, focusing on how the results can be validated by cross-validation, a frequently used validation scheme in machine learning. We also use simulated data to demonstrate the proposed cross-validation scheme's ability to detect overfitting.

Semi-Supervised Machine Learning Algorithm in Near Infrared Spectral Calibration: A Case Study on Diesel Fuels

Semi-Supervised Machine Learning Algorithm in Near Infrared Spectral Calibration: A Case Study on Diesel Fuels