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Yang, Yiming

doi:10.1023/a:1009982220290

Cited by 1,372 publications

(72 citation statements)

References 16 publications

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“…Another measure known by F1 is the average of Precision (Prec) and Recall (Rec) (Yang 1999). Precision is defined as a function of correct positive predictions.…”

Section: Discussionmentioning

confidence: 99%

Multi-label class assignment in land-use modelling

Omrani

Abdallah²,

Charif

et al. 2015

International Journal of Geographical Information Science

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During the last two decades, a variety of models have been applied to understand and predict changes in land use. These models assign a single-attribute label to each spatial unit at any particular time of the simulation. This is not realistic because mixed use of land is quite common. A more detailed classification allowing the modelling of mixed land use would be desirable for better understanding and interpreting the evolution of the use of land. A possible solution is the multi-label (ML) concept where each spatial unit can belong to multiple classes simultaneously. For example, a cluster of summer houses at a lake in a forested area should be classified as water, forest and residential (built-up). The ML concept was introduced recently, and it belongs to the machine learning field. In this article, the ML concept is introduced and applied in land-use modelling. As a novelty, we present a land-use change model that allows ML class assignment using the k nearest neighbour (kNN) method that derives a functional relationship between land use and a set of explanatory variables. A case study with a rich data-set from Luxembourg using biophysical data from aerial photography is described. The model achieves promising results based on the well-known ML evaluation criteria. The application described in this article highlights the value of the multi-label k nearest neighbour method (MLkNN) for land-use modelling.

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“…Another measure known by F1 is the average of Precision (Prec) and Recall (Rec) (Yang 1999). Precision is defined as a function of correct positive predictions.…”

Section: Discussionmentioning

confidence: 99%

Multi-label class assignment in land-use modelling

Omrani

Abdallah²,

Charif

et al. 2015

International Journal of Geographical Information Science

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“…A second but related possible issue with our study is associated with sample number bias [55–57]. We made corrections with weight factors [58,59] and used the multi-class macro- F 1 score [60] to account for the fact that some conditions contained more samples than others, but the predictability of individual conditions nevertheless increased with the number of training samples for that particular condition (S3 Fig). Accuracy limitations could be more thoroughly evaluated through the use of learning curves to determine whether test set accuracies plateau with increasing training set size, but the class imbalance problem and fairly low number of overall samples per condition in our data make it difficult to evaluate accuracies across a broad range of training set sizes.…”

Section: Discussionmentioning

confidence: 99%

“…Instead, we assessed prediction accuracy via F 1 scores, which jointly assess precision and recall. In particular, for predictions of multiple conditions at once, we scored prediction accuracy via the multi-class macro F 1 score [38,60,71] that normalizes individual F 1 scores over individual conditions, i.e., it gives each condition equal weight instead of each sample. There are two different macro F 1 score calculation that have been proposed in the literature.…”

Section: Methodsmentioning

confidence: 99%

Predicting bacterial growth conditions from mRNA and protein abundances

2018

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Cells respond to changing nutrient availability and external stresses by altering the expression of individual genes. Condition-specific gene expression patterns may thus provide a promising and low-cost route to quantifying the presence of various small molecules, toxins, or species-interactions in natural environments. However, whether gene expression signatures alone can predict individual environmental growth conditions remains an open question. Here, we used machine learning to predict 16 closely-related growth conditions using 155 datasets of E. coli transcript and protein abundances. We show that models are able to discriminate between different environmental features with a relatively high degree of accuracy. We observed a small but significant increase in model accuracy by combining transcriptome and proteome-level data, and we show that measurements from stationary phase cells typically provide less useful information for discriminating between conditions as compared to exponentially growing populations. Nevertheless, with sufficient training data, gene expression measurements from a single species are capable of distinguishing between environmental conditions that are separated by a single environmental variable.

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“…However, a system may prefer to differentiate between spammers and manipulators based on the topic of opinions-that is, users who post political opinions are classified as manipulators and the others as spammers. This topic-based classification often utilizes machine learning techniques and has been shown to be successful in various applications [Busemann et al 2000;Yang 1999]. Among the manipulators, nearly 1.43% are incorrectly classified as nonmanipulators.…”

Section: Classification Accuracymentioning

confidence: 99%

Detection of Political Manipulation in Online Communities through Measures of Effort and Collaboration

Lee

2015

ACM Trans. Web

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Online social media allow users to interact with one another by sharing opinions, and these opinions have a critical impact on the way readers think and behave. Accordingly, an increasing number of manipulators deliberately spread messages to influence the public, often in an organized manner. In particular, political manipulation-manipulation of opponents to win political advantage-can result in serious consequences: antigovernment riots can break out, leading to candidates' defeat in an election. A few approaches have been proposed to detect such manipulation based on the level of social interaction (i.e., manipulators actively post opinions but infrequently befriend and reply to other users). However, several studies have shown that the interactions can be forged at a low cost and thus may not be effective measures of manipulation.To go one step further, we collect a dataset for real, large-scale political manipulation, which consists of opinions found on Internet forums. These opinions are divided into manipulators and nonmanipulators. Using this collection, we demonstrate that manipulators inevitably work hard, in teams, to quickly influence a large audience. With this in mind, it could be said that a high level of collaborative efforts strongly indicates manipulation. For example, a group of manipulators may jointly post numerous opinions with a consistent theme and selectively recommend the same, well-organized opinion to promote its rank. We show that the effort measures, when combined with a supervised learning algorithm, successfully identify greater than 95% of the manipulators. We believe that the proposed method will help system administrators to accurately detect manipulators in disguise, significantly decreasing the intensity of manipulation. ACM Reference Format:Sihyung Lee. 2015. Detection of political manipulation in online communities through measures of effort and collaboration.

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Cited by 1,372 publications

References 16 publications

Multi-label class assignment in land-use modelling

Multi-label class assignment in land-use modelling

Predicting bacterial growth conditions from mRNA and protein abundances

Detection of Political Manipulation in Online Communities through Measures of Effort and Collaboration

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