Yuanchen He’s Impressive Third Place Finish in Melbourne University Competition | Kaggle Team Reveals

Yuanchen He’s Impressive Third Place Finish in Melbourne University Competition | Kaggle Team Reveals

Introduction:

Hello, I am Yuanchen He, a senior engineer in McAfee lab with expertise in large data analysis and classification modeling for network security problems. I am excited to share my experience participating in the Kaggle competition and congratulate the winners. Throughout this competition, I gained valuable insights from working with challenging data and reading the winners’ posts. In my approach, I focused on handling categorical features with a high number of values. By removing useless and missing value features, transforming categorical features into binary ones, and generating date-based and indicator features, I achieved a high leaderboard score. However, suspecting information loss, I built classifiers directly on categorical features and combined the top features from both methods, resulting in an improved leaderboard ROC. The best classifiers were obtained training on specific instances, leading to a final AUC of 96.1051 on the test dataset.

Full Article: Yuanchen He’s Impressive Third Place Finish in Melbourne University Competition | Kaggle Team Reveals

Enhancing Network Security with Data Analysis and Classification Modeling: A Report by Yuanchen He

Introduction:
In this report, we will explore how data analysis and classification modeling can greatly enhance network security. The report will highlight the methods used and the insights gained from working on a challenging data set. We will also discuss the winning strategies and their impact on network security.

Method:
We would like to express our gratitude to Kaggle for organizing the competition and congratulate the winners on their success. The competition provided valuable learning experiences and allowed us to gain insights into network security issues. Although we regret not finding the time to write this report last week, we are excited to share our findings now.

Data Analysis and Transformation:
The data provided for the competition included numerous categorical features with a high number of values. At the initial stage, we eliminated irrelevant features and those with nearly 100% missing values. Subsequently, we focused on transforming the categorical features into binary features, where each feature indicated a specific value as either a yes or no. Furthermore, we generated quarter and month features from the start date and created binary indicator features for missing values. We also incorporated other numerical features and dealt with missing values by filling them with the mean. These transformed features were then fed into an R randomForest classifier for Recursive Feature Elimination (RFE). The initial results showed promising accuracy of 94.9x on the competition leaderboard. However, we realized that there might have been some information loss during the feature transformation and selection process.

Direct Classification on Categorical Features:
To address the potential information loss issue, we decided to build classifiers directly on the categorical features without transforming them into binary features. We implemented a simple frequency-based pre-filtering approach, where values that occurred less than 10 times in the data were combined into a common value of “-1”. However, we encountered a limitation with the R randomForest classifier, which can’t handle categorical features with more than 32 values. To overcome this, we split each categorical feature into “sub features”, with each sub feature having no more than 32 values. The values were sorted based on information gain, and the top 31 values were assigned to sub feature 1, the next 31 to sub feature 2, and so on. This feature transformation strategy resulted in a leaderboard ROC score of 94.6x.

Combining Top Features:
To further improve the results, we decided to combine the top features from the previous two methods. By training randomForest classifiers on these combined feature sets, we were able to achieve leaderboard ROC scores ranging from 95.1x to 95.3x, depending on the instances used for training. The most successful classifiers were trained on instances after 0606, after 0612, and after 0706. Notably, the prediction results from these classifiers exhibited distinct differences, making it worthwhile to perform a major voting process. As a result, we achieved our best leaderboard AUC of 95.555, which generalized well to 75% of the test instances, yielding a final AUC of 96.1051.

Conclusion:
In this report, we have demonstrated the effectiveness of data analysis and classification modeling in enhancing network security. We have discussed various methods used, including feature transformation, recursive feature elimination, direct classification on categorical features, and combining top features. These methods have led to significant improvements in terms of accuracy and leaderboard performance. By applying these techniques, organizations can better safeguard their network systems and protect against potential threats.

Disclaimer: This report is based on the personal experiences and insights of the author and does not represent the views of any specific organization or entity.

Summary: Yuanchen He’s Impressive Third Place Finish in Melbourne University Competition | Kaggle Team Reveals

Yuanchen He, a senior engineer in McAfee lab, discusses his approach to a data analysis and classification modeling competition hosted by Kaggle. He initially removed unnecessary features and those with missing values before transforming categorical features into binary features. He also generated additional date-based and indicator features. However, he suspected information loss during this process, so he built classifiers directly on the categorical features, splitting them into sub-features with a limit of 32 values each. Combining the top features from both methods resulted in the best classifiers, achieving a leaderboard ROC of 95.1-95.3. Ultimately, a major voting approach yielded his best leaderboard AUC of 95.555, which generalized well to the test instances.

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