Solve Imbalance Problem Research Articles

A novel term weighting scheme for imbalanced text classification

High dimensional feature is the main problem of text domain. If imbalance class is also found in the context, the classifier’s performance is worsen. Moreover, solving imbalance problem by oversampling method in this circumstance is very difficult to get performance improvement. In this paper, a new term weighting scheme is proposed by combining Term frequency with an average of inverse document frequency factor. We denoted our scheme by TFmeanIDF. Our proposed method has high potential for imbalance text domain with high dimension. No feature selection or oversampling method is required. Extensive comparison results on 7 datasets validate the advantages of TFmeanIDF in terms of F1 score obtained from widely used base classifier such as logistic regression and Support Vector Machines. We found that F1 score of minority class is higher than that of baseline term weighting schemes. Using TFmeanIDF as a term weighting shows promising result of logistic regression and Support Vector Machines.

Chemical characterization and immunomodulatory properties of polysaccharides isolated from probiotic Lactobacillus casei LOCK 0919.

The Lactobacillus casei strain, LOCK 0919, is intended for the dietary management of food allergies and atopic dermatitis (LATOPIC® BIOMED). The use of a probiotic to modulate immune responses is an interesting strategy for solving imbalance problems of gut microflora that may lead to various disorders. However, the exact bacterial signaling mechanisms underlying such modulations are still far from being understood. Here, we investigated variations in the chemical compositions and immunomodulatory properties of the polysaccharides (PS), L919/A and L919/B, which are produced by L. casei LOCK 0919. By virtue of their chemical features, such PS can modulate the immune responses to third-party antigens. Our results revealed that L919/A and L919/B could both modulate the immune response to Lactobacillus planatarum WCFS1, but only L919/B could alter the response of THP-1 cells (in terms of tumor necrosis factor alpha production) to L. planatarum WCFS1 and Escherichia coli Nissle 1917. The comprehensive immunochemical characterization is crucial for the understanding of the biological function as well as of the bacteria–host and bacteria–bacteria cross-talk.

Rule extraction from support vector machines using ensemble learning approach: an application for diagnosis of diabetes.

Diabetes mellitus is a chronic disease and a worldwide public health challenge. It has been shown that 50-80% proportion of T2DM is undiagnosed. In this paper, support vector machines are utilized to screen diabetes, and an ensemble learning module is added, which turns the "black box" of SVM decisions into comprehensible and transparent rules, and it is also useful for solving imbalance problem. Results on China Health and Nutrition Survey data show that the proposed ensemble learning method generates rule sets with weighted average precision 94.2% and weighted average recall 93.9% for all classes. Furthermore, the hybrid system can provide a tool for diagnosis of diabetes, and it supports a second opinion for lay users.

FGsub: Fusarium graminearum protein subcellular localizations predicted from primary structures

BackgroundThe fungal pathogen Fusarium graminearum (telomorph Gibberella zeae) is the causal agent of several destructive crop diseases, where a set of genes usually work in concert to cause diseases to crops. To function appropriately, the F. graminearum proteins inside one cell should be assigned to different compartments, i.e. subcellular localizations. Therefore, the subcellular localizations of F. graminearum proteins can provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus. Unfortunately, there are no subcellular localization information for F. graminearum proteins available now. Computational approaches provide an alternative way to predicting F. graminearum protein subcellular localizations due to the expensive and time-consuming biological experiments in lab.ResultsIn this paper, we developed a novel predictor, namely FGsub, to predict F. graminearum protein subcellular localizations from the primary structures. First, a non-redundant fungi data set with subcellular localization annotation is collected from UniProtKB database and used as training set, where the subcellular locations are classified into 10 groups. Subsequently, Support Vector Machine (SVM) is trained on the training set and used to predict F. graminearum protein subcellular localizations for those proteins that do not have significant sequence similarity to those in training set. The performance of SVMs on training set with 10-fold cross-validation demonstrates the efficiency and effectiveness of the proposed method. In addition, for F. graminearum proteins that have significant sequence similarity to those in training set, BLAST is utilized to transfer annotations of homologous proteins to uncharacterized F. graminearum proteins so that the F. graminearum proteins are annotated more comprehensively.ConclusionsIn this work, we present FGsub to predict F. graminearum protein subcellular localizations in a comprehensive manner. We make four fold contributions to this filed. First, we present a new algorithm to cope with imbalance problem that arises in protein subcellular localization prediction, which can solve imbalance problem and avoid false positive results. Second, we design an ensemble classifier which employs feature selection to further improve prediction accuracy. Third, we use BLAST to complement machine learning based methods, which enlarges our prediction coverage. Last and most important, we predict the subcellular localizations of 12786 F. graminearum proteins, which provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus.