Matching Items (4)
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- All Subjects: Feature Selection
- Creators: Ye, Jieping
Description
Advances in data collection technologies have made it cost-effective to obtain heterogeneous data from multiple data sources. Very often, the data are of very high dimension and feature selection is preferred in order to reduce noise, save computational cost and learn interpretable models. Due to the multi-modality nature of heterogeneous data, it is interesting to design efficient machine learning models that are capable of performing variable selection and feature group (data source) selection simultaneously (a.k.a bi-level selection). In this thesis, I carry out research along this direction with a particular focus on designing efficient optimization algorithms. I start with a unified bi-level learning model that contains several existing feature selection models as special cases. Then the proposed model is further extended to tackle the block-wise missing data, one of the major challenges in the diagnosis of Alzheimer's Disease (AD). Moreover, I propose a novel interpretable sparse group feature selection model that greatly facilitates the procedure of parameter tuning and model selection. Last but not least, I show that by solving the sparse group hard thresholding problem directly, the sparse group feature selection model can be further improved in terms of both algorithmic complexity and efficiency. Promising results are demonstrated in the extensive evaluation on multiple real-world data sets.
ContributorsXiang, Shuo (Author) / Ye, Jieping (Thesis advisor) / Mittelmann, Hans D (Committee member) / Davulcu, Hasan (Committee member) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2014
Description
Alzheimer's Disease (AD) is the most common form of dementia observed in elderly patients and has significant social-economic impact. There are many initiatives which aim to capture leading causes of AD. Several genetic, imaging, and biochemical markers are being explored to monitor progression of AD and explore treatment and detection options. The primary focus of this thesis is to identify key biomarkers to understand the pathogenesis and prognosis of Alzheimer's Disease. Feature selection is the process of finding a subset of relevant features to develop efficient and robust learning models. It is an active research topic in diverse areas such as computer vision, bioinformatics, information retrieval, chemical informatics, and computational finance. In this work, state of the art feature selection algorithms, such as Student's t-test, Relief-F, Information Gain, Gini Index, Chi-Square, Fisher Kernel Score, Kruskal-Wallis, Minimum Redundancy Maximum Relevance, and Sparse Logistic regression with Stability Selection have been extensively exploited to identify informative features for AD using data from Alzheimer's Disease Neuroimaging Initiative (ADNI). An integrative approach which uses blood plasma protein, Magnetic Resonance Imaging, and psychometric assessment scores biomarkers has been explored. This work also analyzes the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Performance of feature selection algorithm is evaluated using the relevance of derived features and the predictive power of the algorithm using Random Forest and Support Vector Machine classifiers. Performance metrics such as Accuracy, Sensitivity and Specificity, and area under the Receiver Operating Characteristic curve (AUC) have been used for evaluation. The feature selection algorithms best suited to analyze AD proteomics data have been proposed. The key biomarkers distinguishing healthy and AD patients, Mild Cognitive Impairment (MCI) converters and non-converters, and healthy and MCI patients have been identified.
ContributorsDubey, Rashmi (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Wu, Tong (Committee member) / Arizona State University (Publisher)
Created2012
Description
The rapid growth in the high-throughput technologies last few decades makes the manual processing of the generated data to be impracticable. Even worse, the machine learning and data mining techniques seemed to be paralyzed against these massive datasets. High-dimensionality is one of the most common challenges for machine learning and data mining tasks. Feature selection aims to reduce dimensionality by selecting a small subset of the features that perform at least as good as the full feature set. Generally, the learning performance, e.g. classification accuracy, and algorithm complexity are used to measure the quality of the algorithm. Recently, the stability of feature selection algorithms has gained an increasing attention as a new indicator due to the necessity to select similar subsets of features each time when the algorithm is run on the same dataset even in the presence of a small amount of perturbation. In order to cure the selection stability issue, we should understand the cause of instability first. In this dissertation, we will investigate the causes of instability in high-dimensional datasets using well-known feature selection algorithms. As a result, we found that the stability mostly data-dependent. According to these findings, we propose a framework to improve selection stability by solving these main causes. In particular, we found that data noise greatly impacts the stability and the learning performance as well. So, we proposed to reduce it in order to improve both selection stability and learning performance. However, current noise reduction approaches are not able to distinguish between data noise and variation in samples from different classes. For this reason, we overcome this limitation by using Supervised noise reduction via Low Rank Matrix Approximation, SLRMA for short. The proposed framework has proved to be successful on different types of datasets with high-dimensionality, such as microarrays and images datasets. However, this framework cannot handle unlabeled, hence, we propose Local SVD to overcome this limitation.
ContributorsAlelyani, Salem (Author) / Liu, Huan (Thesis advisor) / Xue, Guoliang (Committee member) / Ye, Jieping (Committee member) / Zhao, Zheng (Committee member) / Arizona State University (Publisher)
Created2013
Description
Model-based clustering is a sub-field of statistical modeling and machine learning. The mixture models use the probability to describe the degree of the data point belonging to the cluster, and the probability is updated iteratively during the clustering. While mixture models have demonstrated the superior performance in handling noisy data in many fields, there exist some challenges for high dimensional dataset. It is noted that among a large number of features, some may not indeed contribute to delineate the cluster profiles. The inclusion of these “noisy” features will confuse the model to identify the real structure of the clusters and cost more computational time. Recognizing the issue, in this dissertation, I propose a new feature selection algorithm for continuous dataset first and then extend to mixed datatype. Finally, I conduct uncertainty quantification for the feature selection results as the third topic.
The first topic is an embedded feature selection algorithm termed Expectation-Selection-Maximization (ESM) model that can automatically select features while optimizing the parameters for Gaussian Mixture Model. I introduce a relevancy index (RI) revealing the contribution of the feature in the clustering process to assist feature selection. I demonstrate the efficacy of the ESM by studying two synthetic datasets, four benchmark datasets, and an Alzheimer’s Disease dataset.
The second topic focuses on extending the application of ESM algorithm to handle mixed datatypes. The Gaussian mixture model is generalized to Generalized Model of Mixture (GMoM), which can not only handle continuous features, but also binary and nominal features.
The last topic is about Uncertainty Quantification (UQ) of the feature selection. A new algorithm termed ESOM is proposed, which takes the variance information into consideration while conducting feature selection. Also, a set of outliers are generated in the feature selection process to infer the uncertainty in the input data. Finally, the selected features and detected outlier instances are evaluated by visualization comparison.
The first topic is an embedded feature selection algorithm termed Expectation-Selection-Maximization (ESM) model that can automatically select features while optimizing the parameters for Gaussian Mixture Model. I introduce a relevancy index (RI) revealing the contribution of the feature in the clustering process to assist feature selection. I demonstrate the efficacy of the ESM by studying two synthetic datasets, four benchmark datasets, and an Alzheimer’s Disease dataset.
The second topic focuses on extending the application of ESM algorithm to handle mixed datatypes. The Gaussian mixture model is generalized to Generalized Model of Mixture (GMoM), which can not only handle continuous features, but also binary and nominal features.
The last topic is about Uncertainty Quantification (UQ) of the feature selection. A new algorithm termed ESOM is proposed, which takes the variance information into consideration while conducting feature selection. Also, a set of outliers are generated in the feature selection process to infer the uncertainty in the input data. Finally, the selected features and detected outlier instances are evaluated by visualization comparison.
ContributorsFu, Yinlin (Author) / Wu, Teresa (Thesis advisor) / Mirchandani, Pitu (Committee member) / Li, Jing (Committee member) / Pedrielli, Giulia (Committee member) / Arizona State University (Publisher)
Created2020