Early Warning of Gas Concentration in Coal Mines Production Based on Probability Density Machine

Gas explosion has always been an important factor restricting coal mine production safety. The application of machine learning techniques in coal mine gas concentration prediction and early warning can effectively prevent gas explosion accidents. Nearly all traditional prediction models use a regression technique to predict gas concentration. Considering there exist very few instances of high gas concentration, the instance distribution of gas concentration would be extremely imbalanced. Therefore, such regression models generally perform poorly in predicting high gas concentration instances. In this study, we consider early warning of gas concentration as a binary-class problem, and divide gas concentration data into warning class and non-warning class according to the concentration threshold. We proposed the probability density machine (PDM) algorithm with excellent adaptability to imbalanced data distribution. In this study, we use the original gas concentration data collected from several monitoring points in a coal mine in Datong city, Shanxi Province, China, to train the PDM model and to compare the model with several class imbalance learning algorithms. The results show that the PDM algorithm is superior to the traditional and state-of-the-art class imbalance learning algorithms, and can produce more accurate early warning results for gas explosion.

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An empirical comparison on state-of-the-art multi-class imbalance learning algorithms and a new diversified ensemble learning scheme

Knowledge-Based Systems ◽

10.1016/j.knosys.2018.05.037 ◽

2018 ◽

Vol 158 ◽

pp. 81-93 ◽

Cited By ~ 76

Author(s):

Jingjun Bi ◽

Chongsheng Zhang

Keyword(s):

Ensemble Learning ◽

State Of The Art ◽

Learning Algorithms ◽

Class Imbalance ◽

Empirical Comparison ◽

Learning Scheme ◽

Imbalance Learning ◽

Class Imbalance Learning

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Probability Density Machine: A New Solution of Class Imbalance Learning

Scientific Programming ◽

10.1155/2021/7555587 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Ruihan Cheng ◽

Longfei Zhang ◽

Shiqi Wu ◽

Sen Xu ◽

Shang Gao ◽

...

Keyword(s):

Probability Density ◽

Predictive Model ◽

Data Distribution ◽

Class Imbalance ◽

Imbalanced Data ◽

Training Data ◽

Probability Density Estimation ◽

Imbalance Learning ◽

Class Imbalance Learning ◽

The Impact

Class imbalance learning (CIL) is an important branch of machine learning as, in general, it is difficult for classification models to learn from imbalanced data; meanwhile, skewed data distribution frequently exists in various real-world applications. In this paper, we introduce a novel solution of CIL called Probability Density Machine (PDM). First, in the context of Gaussian Naive Bayes (GNB) predictive model, we analyze the reason why imbalanced data distribution makes the performance of predictive model decline in theory and draw a conclusion regarding the impact of class imbalance that is only associated with the prior probability, but does not relate to the conditional probability of training data. Then, in such context, we show the rationality of several traditional CIL techniques. Furthermore, we indicate the drawback of combining GNB with these traditional CIL techniques. Next, profiting from the idea of K-nearest neighbors probability density estimation (KNN-PDE), we propose the PDM which is an improved GNB-based CIL algorithm. Finally, we conduct experiments on lots of class imbalance data sets, and the proposed PDM algorithm shows the promising results.

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