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- Yetian Chen
- 2008

In this paper, we describe the implementation of a set of machine learning techniques: Decision Tree, Perceptrons, Two-layer feed-forward Neural Networks. We describe the application of these techniques to the problem of classifying proteins into their various cellular localization sites based on their amino acid sequences. We evaluate the performance of… (More)

- Yetian Chen
- 2010

In this work, we proposed using Bayesian Networks and Bayesian Model Averaging to address the problem of predicting the cellular localization site of a protein from the its amino acid sequence. We employed the constraint-based algorithm and score-based algorithm to learn a single Bayesian network from data and then used this single Bayesian network for… (More)

- Yetian Chen, Jin Tian
- AAAI
- 2014

We develop an algorithm to find the k-best equivalence classes of Bayesian networks. Our algorithm is capable of finding much more best DAGs than the previous algorithm that directly finds the k-best DAGs [1]. We demonstrate our algorithm in the task of Bayesian model averaging. Empirical results show that our algorithm significantly outperforms the k-best… (More)

- Liyuan Xiao, Yetian Chen, Carl K. Chang
- 2014 IEEE 38th International Computer Software…
- 2014

Bayesian network (BN) classifiers with powerful reasoning capabilities have been increasingly utilized to detect intrusion with reasonable accuracy and efficiency. However, existing BN classifiers for intrusion detection suffer two problems. First, such BN classifiers are often trained from data using heuristic methods that usually select suboptimal models.… (More)

- Yetian Chen
- 2009

In this report, I presented my results to the tasks of 2008 UC San Diego Data Mining Contest. This contest consists of two classification tasks based on data from scientific experiment. The first task is a binary classification task which is to maximize accuracy of classification on an evenly-distributed test data set, given a fully labeled imbalanced… (More)

- Yetian Chen, Jin Tian, Olga Nikolova, Srinivas Aluru
- ArXiv
- 2014

Exact Bayesian structure discovery in Bayesian networks requires exponential time and space. Using dynamic programming (DP), the fastest known serial algorithm computes the exact posterior probabilities of structural features in O(n2 n) time and space, if the number of parents per node or indegree is bounded by a constant d. Here we present a parallel… (More)

- Yetian Chen, Lingjian Meng, Jin Tian
- AISTATS
- 2015

Ancestor relations in Bayesian networks (BNs) encode long-range causal relations among random variables. In this paper, we develop dynamic programming (DP) algorithms to compute the exact posterior probabilities of ancestor relations in Bayesian networks. Previous algorithm by Parviainen and Koivisto (2011) evaluates all possible ancestor relations in time… (More)

- Shekhar Sengar, S. C. Gupta, +18 authors Ou Wu
- 2015

High level security maintenance is very important nowadays for safe and trusted communication over the internet but due to enormous interconnectivity this task has become very complex. Threat of intrusions and misuses is always present in communication over the internet and any other network. These intrusions are occurring at higher rates than before and… (More)

- Yetian Chen, José P. González-Brenes, Jin Tian
- EDM
- 2016

Skill prerequisite information is useful for tutoring systems that assess student knowledge or that provide remediation. These systems often encode prerequisites as graphs designed by subject matter experts in a costly and time-consuming process. In this paper, we introduce Combined student Modeling and prerequisite Discovery (COMMAND), a novel algorithm… (More)

- Yanpeng Zhao, Yetian Chen, Kewei Tu, Jin Tian
- ACML
- 2015

Introduction Bayesian Network (BN) A directed acyclic graph (DAG) where nodes are random variables and directed edges represent probability dependencies among variables BN Structure Learning Firstly construct the topology (structure) of the network Then estimate the parameters (CPDs) given the fixed structure Curriculum Learning (CL) Ideas: learn with the… (More)