Constructing and Evaluating a Churn Prediction Model using Classification of User Types in MMORPG

Icing forecasting for transmission lines is of great significance for anti-icing strategies in power grids, but existing prediction models have some disadvantages such as application limitations, weak generalization, and lack of global prediction ability. To overcome these shortcomings, this paper suggests a new conception about a segmental icing prediction model for transmission lines in which the classification of icing process plays a crucial role. In order to obtain the classification, a hierarchical K-means clustering method is utilized and 11 characteristic parameters are proposed. Based on this method, 97 icing processes derived from the Icing Monitoring System in China Southern Power Grid are clustered into six categories according to their curve shape and the abstracted icing evolution curves are drawn based on the clustering centroid. Results show that the processes of ice events are probably different and the icing process can be considered as a combination of several segments and nodes, which reinforce the suggested conception of the segmental icing prediction model. Based on monitoring data and clustering, the obtained types of icing evolution are more comprehensive and specific, and the work lays the foundation for the model construction and contributes to other fields.

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A novel and convenient variable selection method for choosing effective input variables for telecommunication customer churn prediction model

2009 IEEE International Conference on Systems, Man and Cybernetics ◽

10.1109/icsmc.2009.5346166 ◽

2009 ◽

Cited By ~ 3

Author(s):

Jiayin Qi ◽

Yuanquan Li

Keyword(s):

Variable Selection ◽

Prediction Model ◽

Selection Method ◽

Churn Prediction ◽

Customer Churn ◽

Variable Selection Method ◽

Customer Churn Prediction ◽

Input Variables

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Visual Classification of Music Style Transfer Based on PSO-BP Rating Prediction Model

Complexity ◽

10.1155/2021/9959082 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Tianjiao Li

Keyword(s):

Prediction Model ◽

Real Time ◽

Nonlinear Problems ◽

Style Transfer ◽

Collaborative Filtering Algorithm ◽

Item Attribute ◽

Pixel Color ◽

Rating Prediction ◽

Time Changes

In this paper, based on computer reading and processing of music frequency, amplitude, timbre, image pixel, color filling, and so forth, a method of image style transfer guided by music feature data is implemented in real-time playback, using existing music files and image files, processing and trying to reconstruct the fluent relationship between the two in terms of auditory and visual, generating dynamic, musical sound visualization with real-time changes in the visualization. Although recommendation systems have been well developed in real applications, the limitations of CF algorithms are slowly coming to light as the number of people increases day by day, such as the data sparsity problem caused by the scarcity of rated items, the cold start problem caused by new items and new users. The work is dynamic, with real-time changes in music and sound. Taking portraits as an experimental case, but allowing users to customize the input of both music and image files, this new visualization can provide users with a personalized service of mass customization and generate personalized portraits according to personal preferences. At the same time, we take advantage of the BP neural network’s ability to handle complex nonlinear problems and construct a rating prediction model between the user and item attribute features, referred to as the PSO-BP rating prediction model, by combining the features of global optimization of particle swarm optimization algorithm, and make further improvements based on the traditional collaborative filtering algorithm.

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