Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost’s effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.

/pdf/catboost-for-big-data-an-interdisciplinary-review-utp91h5i0y.pdf

CatBoost for big data: an interdisciplinary review

Tacho-less sparse CNN to detect defects in rotor-bearing systems at varying speed

Crime activity in many cities worldwide causes significant damages to the lives of victims and their surrounding communities. It is a public disorder problem, and big cities experience large amounts of crime events. Spatio-temporal prediction of crimes activity can help the cities to have a better allocation of police resources and surveillance. Deep learning techniques are considered efficient tools to predict future events analyzing the behavior of past ones; however, they are not usually applied to crime event prediction using a spatio-temporal approach. In this paper, a Convolutional Neural Network (CNN) together with a Long-Short Term Memory (LSTM) network (thus CLSTM-NN) are proposed to predict the presence of crime events over the city of Baltimore (USA). In particular, matrices of past crime events are used as input to a CLSTM-NN to predict the presence of at least one event in future days. The model is implemented on two types of events: “street robbery” and “larceny”. The proposed procedure is able to take into account spatial and temporal correlations present in the past data to improve future prediction. The prediction performance of the proposed neural network is assessed under a number of controlled plausible scenarios, using some standard metrics (Accuracy, AUC-ROC, and AUC-PR).

/pdf/spatio-temporal-prediction-of-baltimore-crime-events-using-30v2dzh01l.pdf

Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks

Attention-based learning of self-media data for marketing intention detection

People’s daily information sharing and acquisition through the Internet has become more and more popular. The comprehensive multimodal marketing advertorial generated by ‘We Media’ accounts besides the normal social news is gaining its importance on social media platforms. In order to achieve effective advertising, the marketing intent understanding is a key step towards generating targeted advertising strategies (push advertorials to specific people at a specific time). However, advertorials in real are usually designed to pretend as normal social news with a wide range of contents. This poses big challenges to the platforms on accurately recognizing and analyzing the marketing intents behind the advertorials. As a pioneering study, we address this new problem of multimodalbased marketing intent analysis and answer three core questions: (1) does a piece of social news contain marketing intent? (2) what is the topic of marketing intent? (3) what is the extent of marketing intent? Towards this end, we propose a novel Multimodal-based Marketing Intent Analysis scheme (MMIA) to estimate the marketing intent embedded in the multimodal contents. Specifically, a novel supervised neural autoregressive model (SmiDocNADE) is proposed to enhance the discriminative capacity of the learned hidden features so that a single system is capable of solving the three questions. In order to effectively model inter-correlations between images and text in advertorials, we fuse multimodal data and extract features by Graph Convolution Networks as an enhancement to SmiDocNADE. The extensive evaluations demonstrate the advantages of our proposed system in multimodal-based marketing intent analysis from multiple aspects.

/pdf/multimodal-marketing-intent-analysis-for-effective-targeted-5cm4gzl06o.pdf

Multimodal Marketing Intent Analysis for Effective Targeted Advertising

Social network services for self-media, such as Weibo, Blog, and WeChat Public, constitute a powerful medium that allows users to publish posts every day. Due to insufficient information transparency, malicious marketing of the Internet from self-media posts imposes potential harm on society. Therefore, it is necessary to identify news with marketing intentions for life. We follow the idea of text classification to identify marketing intentions. Although there are some current methods to address intention detection, the challenge is how the feature extraction of text reflects semantic information and how to improve the time complexity and space complexity of the recognition model. To this end, this paper proposes a machine learning method to identify marketing intentions from large-scale We-Media data. First, the proposed Latent Semantic Analysis (LSI)-Word2vec model can reflect the semantic features. Second, the decision tree model is simplified by decision tree pruning to save computing resources and reduce the time complexity. Finally, this paper examines the effects of classifier associations and uses the optimal configuration to help people efficiently identify marketing intention. Finally, the detailed experimental evaluation on several metrics shows that our approaches are effective and efficient. The F1 value can be increased by about 5%, and the running time is increased by 20%, which prove that the newly-proposed method can effectively improve the accuracy of marketing news recognition.

https://www.mdpi.com/1999-5903/11/7/155/pdf

Zhihao Hou

Papers

Stacking-Based Ensemble Learning of Self-Media Data for Marketing Intention Detection

Attention-based learning of self-media data for marketing intention detection

A CLSTM-TMN for marketing intention detection