Support vector machine interpretation

Navia-Vázquez, Ángel; Parrado-Hernández, Emilio

doi:10.1016/j.neucom.2005.12.118

Cited by 27 publications

(20 citation statements)

References 4 publications

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“…For instance, PCA used for logistic regression in E 2 produces components that are delicate to explain causally. Similarly, SVM and NN often lack explanative power, i.e., it is difficult for end users to extract concrete rules as their internal mechanisms are complex to interpret, and the insights into their learning process and decisions are limited [28,34]. In contrast, decision tree learners produce rules that are easier to extract [18].…”

Section: Discussionmentioning

confidence: 99%

Comparing fine-grained source code changes and code churn for bug prediction

Giger

Pinzger

Gall

2011

Proceedings of the 8th Working Conference on Mining Software Repositories

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A significant amount of research effort has been dedicated to learning prediction models that allow project managers to efficiently allocate resources to those parts of a software system that most likely are bug-prone and therefore critical. Prominent measures for building bug prediction models are product measures, e.g., complexity or process measures, such as code churn. Code churn in terms of lines modified (LM) and past changes turned out to be significant indicators of bugs. However, these measures are rather imprecise and do not reflect all the detailed changes of particular source code entities during maintenance activities. In this paper, we explore the advantage of using fine-grained source code changes (SCC) for bug prediction. SCC captures the exact code changes and their semantics down to statement level. We present a series of experiments using different machine learning algorithms with a dataset from the Eclipse platform to empirically evaluate the performance of SCC and LM. The results show that SCC outperforms LM for learning bug prediction models.

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Section: Discussionmentioning

confidence: 99%

Comparing fine-grained source code changes and code churn for bug prediction

Giger

Pinzger

Gall

2011

Proceedings of the 8th Working Conference on Mining Software Repositories

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“…Fish type identification based on fish eye images and useful parameters extracted from these images is the methodology adopted in the designed system. Support vector machine (SVM) has proven to be an effective tool for pattern recognition [1]. The typical method for applying SVM to multiclass problems (N classes) is to construct N number of binary-SVM classifiers, each of which identifies one class among N different classes.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fish Freshness Assessment

Hosseini

Luo

et al. 2008

Journal of Sensors

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Recommended by Subhas MukhopadhyayFish species identification and automated fish freshness assessment play important roles in fishery industry applications. This paper describes a method based on support vector machines (SVMs) to improve the performance of fish identification systems. The result is used for the assessment of fish freshness using artificial neural network (ANN). Identification of the fish species involves processing of the images of fish. The most efficient features were extracted and combined with the down-sampled version of the images to create a 1D input vector. Max-Win algorithm applied to the SVM-based classifiers has enhanced the reliability of sorting to 96.46%. The realisation of Cyranose 320 Electronic nose (E-nose), in order to evaluate the fish freshness in real-time, is experimented. Intelligent processing of the sensor patterns involves the use of a dedicated ANN for each species under study. The best estimation of freshness was provided by the most sensitive sensors. Data was collected from four selected species of fishes over a period of ten days. It was concluded that the performance can be increased using individual trained ANN for each specie. The proposed system has been successful in identifying the number of days after catching the fish with an accuracy of up to 91%.

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“…For example, Cho et al [17] described an approach using a specialized kernel function and a nomogram [4], though this was more a visualization than an interpretation. Navia-Vázquez and Parrado-Hernández [74] described an approach to interpreting SVM classification models based on segmenting the input space using the prototypes extracted from the trained model. In the area of QSAR modeling, Usdun et al [106] described an approach to visualizing and interpreting support vector regression (SVR) models.…”

Section: Interpretation Methodologiesmentioning

confidence: 99%

On the interpretation and interpretability of quantitative structure–activity relationship models

Guha

2008

J Comput Aided Mol Des

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The goal of a quantitative structure-activity relationship (QSAR) model is to encode the relationship between molecular structure and biological activity or physical property. Based on this encoding, such models can be used for predictive purposes. Assuming the use of relevant and meaningful descriptors, and a statistically significant model, extraction of the encoded structure-activity relationships (SARs) can provide insight into what makes a molecule active or inactive. Such analyses by QSAR models are useful in a number of scenarios, such as suggesting structural modifications to enhance activity, explanation of outliers and exploratory analysis of novel SARs. In this paper we discuss the need for interpretation and an overview of the factors that affect interpretability of QSAR models. We then describe interpretation protocols for different types of models, highlighting the different types of interpretations, ranging from very broad, global, trends to very specific, case-by-case, descriptions of the SAR, using examples from the training set. Finally, we discuss a number of case studies where workers have provide some form of interpretation of a QSAR model.

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Support vector machine interpretation

Cited by 27 publications

References 4 publications

Comparing fine-grained source code changes and code churn for bug prediction

Comparing fine-grained source code changes and code churn for bug prediction

Intelligent Fish Freshness Assessment

On the interpretation and interpretability of quantitative structure–activity relationship models

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