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1. Addressing Monotone Likelihood in Duration Modelling of Political Events

   https://doi.org/10.1017/S0007123420000071  

   Anderson, Noel; Bagozzi, Benjamin E.; Koren, Ore (June 2020, British Journal of Political Science)

   This article provides an accessible introduction to the phenomenon of monotone likelihood in duration modeling of political events. Monotone likelihood arises when covariate values are monotonic when ordered according to failure time, causing parameter estimates to diverge toward infinity. Within political science duration model applications, this problem leads to misinterpretation, model misspecification and omitted variable biases, among other issues. Using a combination of mathematical exposition, Monte Carlo simulations and empirical applications, this article illustrates the advantages of Firth's penalized maximum-likelihood estimation in resolving the methodological complications underlying monotone likelihood. The results identify the conditions under which monotone likelihood is most acute and provide guidance for political scientists applying duration modeling techniques in their empirical research. 

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2. A Two-Stage Classification for Dealing with Unseen Clusters in the Testing Data

   https://doi.org/10.6339/24-JDS1140  

   Lee, Jung Wun; Harel, Ofer (January 2024, Journal of Data Science)

   Classification is an important statistical tool that has increased its importance since the emergence of the data science revolution. However, a training data set that does not capture all underlying population subgroups (or clusters) will result in biased estimates or misclassification. In this paper, we introduce a statistical and computational solution to a possible bias in classification when implemented on estimated population clusters. An unseen-cluster problem denotes the case in which the training data does not contain all underlying clusters in the population. Such a scenario may occur due to various reasons, such as sampling errors, selection bias, or emerging and disappearing population clusters. Once an unseen-cluster problem occurs, a testing observation will be misclassified because a classification rule based on the sample cannot capture a cluster not observed in the training data (sample). To overcome such issues, we suggest a two-stage classification method to ameliorate the unseen-cluster problem in classification. We suggest a test to identify the unseen-cluster problem and demonstrate the performance of the two-stage tailored classifier using simulations and a public data example. 

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3. Reconstructing Classification to Enhance Machine-Learning Based Network Intrusion Detection by Embracing Ambiguity

   Song, Chungsik; Fan, Wenjun; Chang, Sang-Yoon; Park, Younghee (December 2020, Silicon Valley Cybersecurity Conference)

   null (Ed.)

   Network intrusion detection systems (IDS) has efficiently identified the profiles of normal network activities, extracted intrusion patterns, and constructed generalized models to evaluate (un)known attacks using a wide range of machine learning approaches. In spite of the effectiveness of machine learning-based IDS, it has been still challenging to reduce high false alarms due to data misclassification. In this paper, by using multiple decision mechanisms, we propose a new classification method to identify misclassified data and then to classify them into three different classes, called a malicious, benign, and ambiguous dataset. In other words, the ambiguous dataset contains a majority of the misclassified dataset and is thus the most informative for improving the model and anomaly detection because of the lack of confidence for the data classification in the model. We evaluate our approach with the recent real-world network traffic data, Kyoto2006+ datasets, and show that the ambiguous dataset contains 77.2% of the previously misclassified data. Re-evaluating the ambiguous dataset effectively reduces the false prediction rate with minimal overhead and improves accuracy by 15%. 

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4. GPSRL: Learning Semi-Parametric Bayesian Survival Rule Lists from Heterogeneous Patient Data

   Shakur, Ameer Hamza; Qian, Xiaoning; Wang, Zhangyang; Mortazavi, Bobak; Huang, Shuai (January 2020, International Conference on Pattern Recognition)

   null (Ed.)

   Survival data is often collected in medical applications from a heterogeneous population of patients. While in the past, popular survival models focused on modeling the average effect of the covariates on survival outcomes, rapidly advancing sensing and information technologies have provided opportunities to further model the heterogeneity of the population as well as the non-linearity of the survival risk. With this motivation, we propose a new semi-parametric Bayesian Survival Rule List model in this paper. Our model derives a rule-based decision-making approach, while within the regime defined by each rule, survival risk is modelled via a Gaussian process latent variable model. Markov Chain Monte Carlo with a nested Laplace approximation on the Gaussian process posterior is used to search over the posterior of the rule lists efficiently. The use of ordered rule lists enables us to model heterogeneity while keeping the model complexity in check. Performance evaluations on a synthetic heterogeneous survival dataset and a real world sepsis survival dataset demonstrate the effectiveness of our model. 

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5. Detecting and correcting misclassified sequences in the large-scale public databases

   https://doi.org/10.1093/bioinformatics/btaa586  

   Bagheri, Hamid; Severin, Andrew; Rajan, Hridesh; Elofsson, Arne (June 2020, Bioinformatics)

   Abstract Motivation As the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for identifying errors in the provided metadata, leading to the potential for error propagation. Previous research on a small subset of the non-redundant (NR) database analyzed misclassification based on sequence similarity. To the best of our knowledge, the amount of misclassification in the entire database has not been quantified. We propose a heuristic method to detect potentially misclassified taxonomic assignments in the NR database. We applied a curation technique and quality control to find the most probable taxonomic assignment. Our method incorporates provenance and frequency of each annotation from manually and computationally created databases and clustering information at 95% similarity. Results We found more than 2 million potentially taxonomically misclassified proteins in the NR database. Using simulated data, we show a high precision of 97% and a recall of 87% for detecting taxonomically misclassified proteins. The proposed approach and findings could also be applied to other databases. Availability Source code, dataset, documentation, Jupyter notebooks, and Docker container are available at https://github.com/boalang/nr. Supplementary information Supplementary data are available at Bioinformatics online. 

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