Leveraging Higher Order Dependencies between Features for Text Classification

Ganiz, Murat Can; Lytkin, Nikita I.; Pottenger, William M.

doi:10.1007/978-3-642-04180-8_42

Cited by 21 publications

(20 citation statements)

References 15 publications

(33 reference statements)

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“…A simplified model, the HOTK, uses higher-order paths between terms. In this sense, it is similar to the previously proposed term-based higher-order learning algorithms Higher-Order Naïve Bayes (HONB) (Ganiz et al, 2009) and Higher-Order Smoothing (HOS) (Poyraz et al, 2012(Poyraz et al, , 2014.…”

Section: Introductionsupporting

confidence: 66%

“…Based on these work, the authors in Ganiz et al (2009Ganiz et al ( , 2011) built a new Bayesian classification framework called Higher-Order Naive Bayes (HONB) which presents that words in documents are strongly connected by such higher-order paths and that they can be exploited in order to get better performance for classification. Both HONB (Ganiz et al, 2009) and HOS (Poyraz et al, 2012(Poyraz et al, , 2014 are based on Naïve Bayes.…”

Section: Semantic Kernels For Text Classificationmentioning

confidence: 99%

“…Benefits of using on higher-order paths between documents (Altınel et al, 2014a) and between terms (Altınel et al, 2014b;Ganiz et al, 2009;Poyraz et al, 2014) are demonstrated in Fig. 1.…”

Section: Semantic Kernels For Text Classificationmentioning

confidence: 99%

“…Additionally, we filter rare terms which occur in less than three documents. We also apply attribute selection and select the most informative 2,000 terms using Information Gain as described in Altınel et al (2013Altınel et al ( , 2014aAltınel et al ( , 2014b, Ganiz et al, 2009Ganiz et al, , (2011 and Poyraz et al (2012Poyraz et al ( , 2014. This preprocessing increase the performance of the classifier models by reducing the noise.…”

Section: Experiments Setupmentioning

confidence: 99%

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A corpus-based semantic kernel for text classification by using meaning values of terms

Altınel

Ganiz

Di̇ri̇

2015

Engineering Applications of Artificial Intelligence

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a b s t r a c tText categorization plays a crucial role in both academic and commercial platforms due to the growing demand for automatic organization of documents. Kernel-based classification algorithms such as Support Vector Machines (SVM) have become highly popular in the task of text mining. This is mainly due to their relatively high classification accuracy on several application domains as well as their ability to handle high dimensional and sparse data which is the prohibitive characteristics of textual data representation. Recently, there is an increased interest in the exploitation of background knowledge such as ontologies and corpus-based statistical knowledge in text categorization. It has been shown that, by replacing the standard kernel functions such as linear kernel with customized kernel functions which take advantage of this background knowledge, it is possible to increase the performance of SVM in the text classification domain. Based on this, we propose a novel semantic smoothing kernel for SVM. The suggested approach is based on a meaning measure, which calculates the meaningfulness of the terms in the context of classes. The documents vectors are smoothed based on these meaning values of the terms in the context of classes. Since we efficiently make use of the class information in the smoothing process, it can be considered a supervised smoothing kernel. The meaning measure is based on the Helmholtz principle from Gestalt theory and has previously been applied to several text mining applications such as document summarization and feature extraction. However, to the best of our knowledge, ours is the first study to use meaning measure in a supervised setting to build a semantic kernel for SVM. We evaluated the proposed approach by conducting a large number of experiments on well-known textual datasets and present results with respect to different experimental conditions. We compare our results with traditional kernels used in SVM such as linear kernel as well as with several corpus-based semantic kernels. Our results show that classification performance of the proposed approach outperforms other kernels.

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Section: Introductionsupporting

confidence: 66%

Section: Semantic Kernels For Text Classificationmentioning

confidence: 99%

Section: Semantic Kernels For Text Classificationmentioning

confidence: 99%

Section: Experiments Setupmentioning

confidence: 99%

See 2 more Smart Citations

A corpus-based semantic kernel for text classification by using meaning values of terms

Altınel

Ganiz

Di̇ri̇

2015

Engineering Applications of Artificial Intelligence

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“…Identifying mislabeled or anomalous shipments through scrutiny of manifest data is one step in a multi-layer inspection process for containers arriving at ports described in [27.] Other relevant work on risk scoring and anomaly detection from manifest data is found in [5] and [13] …”

Section: Introductionmentioning

confidence: 99%

Efficient sequential decision-making algorithms for container inspection operations

Madigan

Mittal

Roberts

2011

Naval Research Logistics

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Following work of Stroud and Saeger [26] and Anand et al.[1], we formulate a port of entry inspection sequencing task as a problem of finding an optimal binary decision tree for an appropriate Boolean decision function. We report on new algorithms for finding such optimal trees that are more efficient computationally than those presented by Stroud and Saeger and Anand et al.We achieve these efficiencies through a combination of specific numerical methods for finding optimal thresholds for sensor functions and two novel binary decision tree search algorithms that operate on a space of potentially acceptable binary decision trees. The improvements enable us to analyze substantially larger applications than was previously possible.

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Sensor Management Problems of Nuclear Detection

Carpenter

Cheng

Roberts

et al. 2011

Springer Series in Reliability Engineering

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Terrorist nuclear attack is a potentially devastating threat to homeland security. It is increasingly important to have the capability to intercept illicit nuclear materials entering the country and to monitor for nuclear threats emerging from within. This article describes a variety of approaches to sensor management in a multi-institution project on nuclear detection, which is based at Rutgers University and supported by the US Department of Homeland Security Domestic Nuclear Detection Office in collaboration with the US National Science Foundation. These approaches revolve around formulating the related problems using precise mathematical language and then developing tools of the mathematical sciences to solve them. The article provides an overview and summary of the project. In so doing, it touches on four themes that have emerged as the areas of greatest emphasis in the project: 1) methods to exploit data from radiation sensors and shipping manifests for classification and decision making; 2) ways to optimize sequential decisions in layered inspection processes; 3) detection using a fleet of mobile radiation sensors; and 4) data sampling strategies for nuclear detection.

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Leveraging Higher Order Dependencies between Features for Text Classification

Cited by 21 publications

References 15 publications

A corpus-based semantic kernel for text classification by using meaning values of terms

A corpus-based semantic kernel for text classification by using meaning values of terms

Efficient sequential decision-making algorithms for container inspection operations

Sensor Management Problems of Nuclear Detection

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