Jiayu Zhou | Michigan State University (original) (raw)

Papers by Jiayu Zhou

The patient risk prediction model aims at assessing the risk of a patient in developing a target ... more The patient risk prediction model aims at assessing the risk of a patient in developing a target disease based on his/her health profile. As electronic health records (EHRs) become more prevalent, a large number of features can be constructed in order to characterize patient profiles. This wealth of data provides unprecedented opportunities for data mining researchers to address important biomedical questions. Practical data mining challenges include: How to correctly select and rank those features based on their prediction power? What predictive model performs the best in predicting a target disease using those features?

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13, 2013

ABSTRACT Traditionally, feature construction and feature selection are two important but separate... more ABSTRACT Traditionally, feature construction and feature selection are two important but separate processes in data mining. However, many real world applications require an integrated approach for creating, refining and selecting features. To address this problem, we propose FeaFiner (short for Feature Refiner), an efficient formulation that simultaneously generalizes low-level features into higher level concepts and then selects relevant concepts based on the target variable. Specifically, we formulate a double sparsity optimization problem that identifies groups in the low-level features, generalizes higher level features using the groups and performs feature selection. Since in many clinical researches non- overlapping groups are preferred for better interpretability, we further improve the formulation to generalize features using mutually exclusive feature groups. The proposed formulation is challenging to solve due to the orthogonality constraints, non-convexity objective and non-smoothness penal- ties. We apply a recently developed augmented Lagrangian method to solve this formulation in which each subproblem is solved by a non-monotone spectral projected gradient method. Our numerical experiments show that this approach is computationally efficient and also capable of producing solutions of high quality. We also present a generalization bound showing the consistency and the asymptotic behavior of the learning process of our proposed formulation. Finally, the proposed FeaFiner method is validated on Alzheimer's Disease Neuroimaging Initiative dataset, where low-level biomarkers are automatically generalized into robust higher level concepts which are then selected for predicting the disease status measured by Mini Mental State Examination and Alzheimer's Disease Assessment Scale cognitive subscore. Compared to existing predictive modeling methods, FeaFiner provides intuitive and robust feature concepts and competitive predictive accuracy.

International Journal of Cognitive Informatics and Natural Intelligence, 2010

... Liu (2005, 2006) used the isa pattern to extract Chinese hyponymy relations from unstructur... more ... Liu (2005, 2006) used the isa pattern to extract Chinese hyponymy relations from unstructured Web corpus, and have been proven to have a ... In this study, we have found a special kind of Chinese hyponymy relationship, called lexi-cal hyponymy, which is of great importance ...

We present a system to translate natural language sentences to formulas in a formal or a knowledg... more We present a system to translate natural language sentences to formulas in a formal or a knowledge representation language. Our system uses two inverse lambda-calculus operators and using them can take as input the semantic representation of some words, phrases and sentences and from that derive the semantic representation of other words and phrases. Our inverse lambda operator works on

Proceedings of the British Machine Vision Conference 2014, 2014

Proceedings of the 2015 SIAM International Conference on Data Mining, 2015

Recommending new items to existing users has remained a challenging problem due to absence of use... more Recommending new items to existing users has remained a challenging problem due to absence of user's past preferences for these items. The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem. These methods rely on similarities between the target item and user's previous preferred items. While computing similarities based on item features, these methods overlook the interactions among the features of the items and consider them independently. Modeling interactions among features can be helpful as some features, when considered together, provide a stronger signal on the relevance of an item when compared to case where features are considered independently. To address this important issue, in this work we introduce the Feature-based factorized Bilinear Similarity Model (FBSM), which learns factorized bilinear similarity model for Top-n recommendation of new items, given the information about items preferred by users in past as well as the features of these items. We carry out extensive empirical evaluations on benchmark datasets, and we find that the proposed FBSM approach improves upon traditional non-collaborative methods in terms of recommendation performance. Moreover, the proposed approach also learns insightful interactions among item features from data, which lead to deep understanding on how these interactions contribute to personalized recommendation.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2014

Complex diseases such as major depression affect people over time in complicated patterns. Longit... more Complex diseases such as major depression affect people over time in complicated patterns. Longitudinal data analysis is thus crucial for understanding and prognosis of such diseases and has received considerable attention in the biomedical research community. Traditional classification and regression methods have been commonly applied in a simple (controlled) clinical setting with a small number of time points. However, these methods cannot be easily extended to the more general setting for longitudinal analysis, as they are not inherently built for time-dependent data. Functional regression, in contrast, is capable of identifying the relationship between features and outcomes along with time information by assuming features and/or outcomes as random functions over time rather than independent random variables. In this paper, we propose a novel sparse generalized functional linear model for the prediction of treatment remission status of the depression participants with longitudina...

2013 IEEE 13th International Conference on Data Mining, 2013

Recovering a matrix from a sampling of its entries is a problem of rapidly growing interest and h... more Recovering a matrix from a sampling of its entries is a problem of rapidly growing interest and has been studied under the name of matrix completion. It occurs in many areas of engineering and applied science. In most machine learning and data mining applications, it is possible to leverage the expertise of human oracles to improve the performance of the system. It is therefore natural to extend this idea of "human-in-theloop" to the matrix completion problem. However, considering the enormity of data in the modern era, manually completing all the entries in a matrix will be an expensive process in terms of time, labor and human expertise; human oracles can only provide selective supervision to guide the solution process. Thus, appropriately identifying a subset of missing entries (for manual annotation) in an incomplete matrix is of paramount practical importance; this can potentially lead to better reconstructions of the incomplete matrix with minimal human effort. In this paper, we propose novel algorithms to address this issue. Since the query locations are actively selected by the algorithms, we refer to these methods as active matrix completion algorithms. The proposed techniques are generic and the same frameworks can be used in a wide variety of applications including recommendation systems, transductive / multi-label active learning, active learning in regression and active feature acquisition among others. Our extensive empirical analysis on several challenging real-world datasets certify the merit and versatility of the proposed frameworks in efficiently exploiting human intelligence in data mining / machine learning applications.