Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language

Daelemans, Walter; Hoste, Véronique; Meulder, Fien De; Naudts, Bart

doi:10.1007/978-3-540-39857-8_10

Cited by 58 publications

(38 citation statements)

References 16 publications

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“…Correlation was used as the filtering metric to search for optimal subsets of features. Given that FS techniques have biases known to affect the variable selection optimization method (30,54), several FS methods were applied.…”

Section: Methodsmentioning

confidence: 99%

Coevolution of the Hepatitis C Virus Polyprotein Sites in Patients on Combined Pegylated Interferon and Ribavirin Therapy

Lara

Xia

Purdy

et al. 2011

J Virol

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Genotype-specific sensitivity of the hepatitis C virus (HCV) to interferon-ribavirin (IFN-RBV) combination therapy and reduced HCV response to IFN-RBV as infection progresses from acute to chronic infection suggest that HCV genetic factors and intrahost HCV evolution play important roles in therapy outcomes. HCV polyprotein sequences (n ‫؍‬ 40) from 10 patients with unsustainable response (UR) (breakthrough and relapse) and 10 patients with no response (NR) following therapy were identified through the Virahep-C study. Bayesian networks (BNs) were constructed to relate interrelationships among HCV polymorphic sites to UR/NR outcomes. All models showed an extensive interdependence of HCV sites and strong connections (P < 0.003) to therapy response. Although all HCV proteins contributed to the networks, the topological properties of sites differed among proteins. E2 and NS5A together contributed ϳ40% of all sites and ϳ62% of all links to the polyprotein BN. The NS5A BN and E2 BN predicted UR/NR outcomes with 85% and 97.5% accuracy, respectively, in 10-fold cross-validation experiments. The NS5A model constructed using physicochemical properties of only five sites was shown to predict the UR/NR outcomes with 83.3% accuracy for 6 UR and 12 NR cases of the HALT-C study. Thus, HCV adaptation to IFN-RBV is a complex trait encoded in the interrelationships among many sites along the entire HCV polyprotein. E2 and NS5A generate broad epistatic connectivity across the HCV polyprotein and essentially shape intrahost HCV evolution toward the IFN-RBV resistance. Both proteins can be used to accurately predict the outcomes of IFN-RBV therapy.Hepatitis C virus (HCV) is the major etiologic agent of blood-borne non-A, non-B hepatitis (25). Chronic HCV infection is an established risk factor for the development of liver diseases, such as fibrosis, cirrhosis, and hepatocellular carcinoma (33,124,125). Approximately 70% to 80% of HCVinfected patients fail to clear the virus and progress to chronicity (89a). At present, there are no preventive vaccines against HCV. The current, accepted therapeutic approach to treating chronic hepatitis C infection involves a 24-or 48-week course of pegylated alpha interferon (IFN-␣) combined with ribavirin (RBV) (i.e. IFN-RBV therapy) (48, 52). Because only 50% to 70% of chronically infected patients develop a sustained virologic response (SVR) to this treatment (48,52,55,80) and because patient intolerance to such therapy is common (61, 68, 120), the development and application of other therapeutic approaches using antiviral compounds that act against HCV more efficaciously and yet generate lower rates of adverse effects are major clinical management and public health objectives. Therapeutic failure presents in two forms: (i) complete resistance to treatment (no response [NR]) and (ii) unsustainable response (UR), which is characterized by an increase in HCV load observed during therapy after an initial period of decline in viral load (breakthrough) or observed after cessation of therapy (relapse)...

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Section: Methodsmentioning

confidence: 99%

Coevolution of the Hepatitis C Virus Polyprotein Sites in Patients on Combined Pegylated Interferon and Ribavirin Therapy

Lara

Xia

Purdy

et al. 2011

J Virol

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“…Instead of just optimizing the parameters of the model for the full feature subset, we now need to find the optimal model parameters for the optimal feature subset [1], as there is no guarantee that the optimal parameters for the full feature set are equally optimal for the optimal feature subset. http://ijacsa.thesai.org/ There are three types of feature subset selection approaches: depending on how they combine the feature selection search with the construction of the classification model: filters, wrappers and embedded methods which perform the features selection process as an integral part of a machine learning (ML) algorithm.…”

Section: Feature Selection Techniquesmentioning

confidence: 99%

A study on Feature Selection Techniques in Bio-Informatics

Devi¹,

Rajagopalan²

2011

IJACSA

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Abstract-The availability of massive amounts of experimental data based on genome-wide studies has given impetus in recent years to a large effort in developing mathematical, statistical and computational techniques to infer biological models from data. In many bioinformatics problems the number of features is significantly larger than the number of samples (high feature to sample ratio datasets) and feature selection techniques have become an apparent need in many bioinformatics applications. This article provides the reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, discussing its uses, common and upcoming bioinformatics applications.

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“…The latter aims to select the most relevant features either to remove the least useful, and thus improve efficiency, or to improve accuracy, for example (Mihalcea, 2002;Decadt et al, 2004). Daelemans et al (2003) show that feature selection, together with parameter optimisation, plays an important role in the use of machine learning algorithms for NLP applications, including WSD. However, feature selection does not say much about the types of KSs that are most useful for the WSD problem in general.…”

Section: Related Workmentioning

confidence: 99%

Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation

Specia

Stevenson

Nunes

2009

Lang Resources & Evaluation

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Published paperSpecia, L., Stevenson, M., das Gracas, M., Nunes, V. (2010) Abstract. Corpus-based techniques have proved to be very beneficial in the development of efficient and accurate approaches to word sense disambiguation (WSD) despite the fact that they generally represent relatively shallow knowledge. It has always been thought, however, that WSD could also benefit from deeper knowledge sources. We describe a novel approach to WSD using inductive logic programming to learn theories from first-order logic representations that allows corpus-based evidence to be combined with any kind of background knowledge. This approach has been shown to be effective over several disambiguation tasks using a combination of deep and shallow knowledge sources. Is it important to understand the contribution of the various knowledge sources used in such a system. This paper investigates the contribution of nine knowledge sources to the performance of the disambiguation models produced for the SemEval-2007 English lexical sample task. The outcome of this analysis will assist future work on WSD in concentrating on the most useful knowledge sources.

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Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language

Cited by 58 publications

References 16 publications

Coevolution of the Hepatitis C Virus Polyprotein Sites in Patients on Combined Pegylated Interferon and Ribavirin Therapy

Coevolution of the Hepatitis C Virus Polyprotein Sites in Patients on Combined Pegylated Interferon and Ribavirin Therapy

A study on Feature Selection Techniques in Bio-Informatics

Assessing the contribution of shallow and deep knowledge sources for word sense disambiguation

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