Graph-based Classification Algorithm Research Articles

Linking personality types to depressive symptoms: A prospective typology based on neuroticism, extraversion and conscientiousness

From a traditional variable-centered perspective, the personality traits specifically linked to depressive symptoms are neuroticism, extraversion, and conscientiousness (NEC). Few studies have considered the interactive effects of personality traits within individuals from the taxonomic perspective. We explored novel NEC personality subtypes in general and subthreshold depressive subjects by using graph-based classification algorithms and multiple external validators. Personality and depressive symptoms were assessed in 1055 healthy subjects (150 with subthreshold depression) using the NEO-PI-R and BDI at baseline. NEC personality data were used to identify subtypes in healthy and subthreshold depressive samples, and external validators, including current and longitudinal depressive symptoms and seven subcortical gray matter volumes, were examined. Four novel NEC personality types in the general sample showed superior current and longitudinal behavioral validation of depressive symptoms as well as some discrimination in biological indicators (putamen, nucleus accumbens, and amygdala). Four profiles defined in subthreshold depression selectively exhibited meaningful differences in longitudinal depressive symptoms. In both samples, some types adhere to the principles previously described NEC three-way interaction. The resulting typology, especially the four types in the general population, linked depressive symptoms provided a superior description of within-person organization of neuroticism, extraversion, and conscientiousness.

An enhanced noise resilient K-associated graph classifier

In this paper, we propose a non-parametric, noise resilient, graph-based classification algorithm. By modifying the training phase of the k-associated optimal graph algorithm, and proposing a new labeling algorithm in the testing phase, we introduce a novel approach that is robust in the presence of different level of noise. In designing the proposed classification method, each class of dataset is represented by a set of sub-graphs (components), and a new extension of the k-associated optimal graph algorithm is introduced in the training phase to combine the smaller components. With this enhancement, we demonstrate that our algorithm distinguishes between noisy and non-noisy sub-graphs. Moreover, in the testing phase, we combine relational data, such as the degree of relevancy, with non-relational attributes, such as distance, for each sample in a graph to make the proposed algorithm less sensitive to noise. Gravity formula is the main concept behind the proposed test sample with various modifications to tailor it to the arbitrary shape and non-uniform sample scattering of the graph structure. We compare the proposed method with a graph-based classifier, as well as two other well-known classifiers, namely, Decision Tree and Multi-Class Support Vector Machine. Confirmed by the t-Test score, our proposed method shows a superior performance in the presence of different levels of noise on various datasets from the UCI repository. At a noise level of 5% or higher, the proposed algorithm performs, in average, 7% better than the graph-based classification algorithm. At a noise level of 20%, the proposed method performs, in average, 5% better than Decision Tree and multi-class SVM.

Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation

Graph-based classification algorithms have gained increasing attention in semi-supervised classification. Nevertheless, the graph cannot fully represent the inherent spatial distribution of the data. In this paper, a new classification methodology based on the spatial-spectral Label Propagation is proposed for semi-supervised classification of hyperspectral imagery. The spatial information was used in two aspects: on the one hand, the spatial features extracted by a 2-D Gabor filter were stacked with spectral features; on the other hand, the width of the Gaussian function, which was used to construct graph, was determined with an adaptive method. Subsequently, the unlabeled samples from the spatial neighbors of the labeled samples were selected and the spatial graph was constructed based on spatial smoothness. Finally, labels were propagated from labeled samples to unlabeled samples with spatial-spectral graph to update the training set for a basic classifier (e.g., Support Vector Machine, SVM). Experiments on four hyperspectral datasets show that the proposed Spatial-Spectral Label Propagation based on the SVM (SS-LPSVM) can effectively represent the spatial information in the framework of semi-supervised learning and consistently produces greater classification accuracy than the standard SVM, the Laplacian Support Vector Machine (LapSVM), Transductive Support Vector Machine (TSVM) and the Spatial-Contextual Semi-Supervised Support Vector Machine (SCS3VM).

RF_MISMOC: Improvement of MISMOC graph based classification algorithm

In recent years, the graph mining has gained much attention in the area of data mining. A novel technique called mining interesting substructures in molecular data for classification (MISMOC) is one of the most efficient algorithms in graph based classification. In this paper, we propose a novel technique called RF_MISMOC (Relative Frequency MISMOC) for computing interestingness of patterns by considering relative frequency of patterns in each class. In addition, we have improved the performance of the base algorithm by selecting equal numbers of interesting indicator patterns of classes and also determining optimum threshold value for selection of indicator patterns. The experimental results demonstrate that, the proposed algorithm has improved efficiency of the base algorithm.

Multi-label literature classification based on the Gene Ontology graph

BackgroundThe Gene Ontology is a controlled vocabulary for representing knowledge related to genes and proteins in a computable form. The current effort of manually annotating proteins with the Gene Ontology is outpaced by the rate of accumulation of biomedical knowledge in literature, which urges the development of text mining approaches to facilitate the process by automatically extracting the Gene Ontology annotation from literature. The task is usually cast as a text classification problem, and contemporary methods are confronted with unbalanced training data and the difficulties associated with multi-label classification.ResultsIn this research, we investigated the methods of enhancing automatic multi-label classification of biomedical literature by utilizing the structure of the Gene Ontology graph. We have studied three graph-based multi-label classification algorithms, including a novel stochastic algorithm and two top-down hierarchical classification methods for multi-label literature classification. We systematically evaluated and compared these graph-based classification algorithms to a conventional flat multi-label algorithm. The results indicate that, through utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods can significantly improve predictions of the Gene Ontology terms implied by the analyzed text. Furthermore, the graph-based multi-label classifiers are capable of suggesting Gene Ontology annotations (to curators) that are closely related to the true annotations even if they fail to predict the true ones directly. A software package implementing the studied algorithms is available for the research community.ConclusionThrough utilizing the information from the structure of the Gene Ontology graph, the graph-based multi-label classification methods have better potential than the conventional flat multi-label classification approach to facilitate protein annotation based on the literature.