Browsing by Subject "Voting"

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Open Access
Classification by feature partitioning
(Springer/, 1996) Guvenir, H. A.; Şirin, İ.
This paper presents a new form of exemplar-based learning, based on a representation scheme called jfaliirf parluinning, and a panitular implementation of this technique called CFF (for Classification by feature Partioning). Learning in CFP is accomplished by storing the objects separately in each (tenure dimension as disjoint sets of values called segments A segment is; expanded through generalization or specialized by dividing in into sub-segments. Cklassification is based on a weighted voting among the individual productions of the features, which are simply the class values of the segments corresponding to the values of a test instance fur each feature An empirical evaluation of CFP and its comparison with two other classification techniques, lhai consider each feature separately are given. © 1996 Kluwer Academic Publishers,.
Open Access
Comparative analysis of different approaches to target classification and localization with sonar
(IEEE, 2001-08) Ayrulu, Birsel; Barshan, Billur
The comparison of different classification and fusion techniques was done for target classification and localization with sonar. Target localization performance of artificial neural networks (ANN) was found to be better than the target differentiation algorithm (TDA) and fusion techniques. The target classification performance of non-parametric approaches was better than that of parameterized density estimator (PDE) using homoscedastic and heteroscedastic NM for statistical pattern recognition techniques.
Open Access
Comparison of two methods of surface profile extraction from multiple ultrasonic range measurements
(Institute of Physics Publishing, 2000) Barshan, B.; Backent, D.
Two novel methods for surface profile extraction based on multiple ultrasonic range measurements are described and compared. One of the methods employs morphological processing techniques, whereas the other employs a spatial voting scheme followed by simple thresholding. Morphological processing exploits neighbouring relationships between the pixels of the generated arc map. On the other hand, spatial voting relies on the number of votes accumulated in each pixel and ignores neighbouring relationships. Both approaches are extremely flexible and robust, in addition to being simple and straightforward. They can deal with arbitrary numbers and configurations of sensors as well as synthetic arrays. The methods have the intrinsic ability to suppress spurious readings, crosstalk and higher-order reflections, and process multiple reflections informatively. The performances of the two methods are compared on various examples involving both simulated and experimental data. The morphological processing method outperforms the spatial voting method in most cases with errors reduced by up to 80%. The effect of varying the measurement noise and surface roughness is also considered. Morphological processing is observed to be superior to spatial voting under these conditions as well.
Open Access
Critical discourse analysis of tweets and entries of dissidents in Turkey: the irresistible lure of voting
(2019-08) İnce, Tuğçe
Turkey had 12 major political elections in last 10 years. Such intensive election environment had profound impacts on voters. Especially after the elections conducted in 2018 and 2019 many dissident voters first stated on online websites that they will abstain from political elections, and yet later on stated that they actually voted after all. In this paper, through a discourse analysis of statements of dissident voters on online platforms such as Twitter and Ekşi Sözlük, I will demonstrate what accounts for turnout among dissidents in Turkey. There are 3 main factors, which are political, social and psychological factors, revealed around the dissidents’ statements. According to 750 online posts on Twitter and Ekşi Sözlük out of countless many, most of the dissidents went to the ballot box as a reaction to polarized political environment generated by the ruling party AKP and to say that they exist and will not yield to black propaganda. In relation with political factors, such as polarized political environment and political figures which attracted dissidents, voters cast their vote since their social circles (families and friends) influenced them to do so. As a third factor, psychological factors brought dissidents to the ballot box by mostly awakening their feelings of remorse and gratitude. In the light of my findings it is important to see that in a hybrid regime like Turkey voting is not only a fundamental act of political participation but also a struggle for life for the opposition.
Open Access
Diagnosis of gastric carcinoma by classification on feature projections
(Elsevier, 2004) Güvenir, H. A.; Emeksiz, N.; İkizler, N.; Örmeci, N.
A new classification algorithm, called benefit maximizing classifier on feature projections (BCFP), is developed and applied to the problem of diagnosis of gastric carcinoma. The domain contains records of patients with known diagnosis through gastroscopy results. Given a training set of such records, the BCFP classifier learns how to differentiate a new case in the domain. BCFP represents a concept in the form of feature projections on each feature dimension separately. Classification in the BCFP algorithm is based on a voting among the individual predictions made on each feature. In the gastric carcinoma domain, a lesion can be an indicator of one of nine different levels of gastric carcinoma, from early to late stages. The benefit of correct classification of early levels is much more than that of late cases. Also, the costs of wrong classifications are not symmetric. In the training phase, the BCFP algorithm learns classification rules that maximize the benefit of classification. In the querying phase, using these rules, the BCFP algorithm tries to make a prediction maximizing the benefit. A genetic algorithm is applied to select the relevant features. The performance of the BCFP algorithm is evaluated in terms of accuracy and running time. The rules induced are verified by experts of the domain. © 2004 Elsevier B.V. All rights reserved.
Open Access
Manipulation via information in large elections
(2006) Sezer, İlhan
This thesis studies manipulations of equilibria by candidates in two-alternative elections along with their effects on voter turnout, winner of the election and social welfare where voters have common values, and both voting and manipulating are costly. We show that manipulation is not desirable for the society, and the candidates’ incentives for manipulating can be mitigated by appropriately sequencing the order of manipulations. We present some results of a manipulation game which may rather unexpected under the assumption that the candidates have prior beliefs about each others’ manipulations. Finally we determine the set of manipulations which can be prevented by informed voters for a given composition of society.
Open Access
Non-incremental classification learning algorithms based on voting feature intervals
(1997-08) Demiröz, Gülşen
Learning is one of the necessary abilities of an intelligent agent. This thesis proposes several learning algorithms for multi-concept descriptions in the form of feature intervals, called Voting Feature Intervals (VFI) algorithms. These algorithms are non-incremental classification learning algorithms, and use feature projection based knowledge representation for the classification knowledge induced from a set of preclassified examples. The concept description learned is a set of intervals constructed separately for each feature. Each interval carries classification information for all classes. The classification of an unseen instance is based on a voting scheme, where each feature distributes its vote among all classes. Empirical evaluation of the VFI algorithms has shown that they are the best performing algorithms among other previously developed feature projection based methods in term of classification accuracy. In order to further improve the accuracy, genetic algorithms are developed to learn the optimum feature weights for any given classifier. Also a new crossover operator, called continuous uniform crossover, to be used in this weight learning genetic algorithm is proposed and developed during this thesis. Since the explanation ability of a learning system is as important as its accuracy, VFI classifiers are supplemented with a facility to convey what they have learned in a comprehensible way to humans.
Open Access
Race, ethnicity, and political behavior
(Oxford University Press, 2017) Just, Aida; Thompson, W. R.
Whether as a consequence of colonialism or more recent international migration, ethnic diversity has become a prominent feature of many contemporary democracies. Given the importance of ethnicity in structuring people’s identities, scholars have sought to incorporate ethnicity in their models of people’s political behavior. Studies focusing on individual support for group interests among ethnic minority members find that higher socioeconomic status generally leads to a reduced emphasis on ethnicity in forming individual political opinions. However, this relationship is often considerably weaker among ethnic minorities with frequent experiences of discrimination, pessimistic assessments of equal opportunities in a country, and social pressures from group members to comply with group norms. Research also shows that, in comparison to majority populations, members of ethnic minorities are generally less active in politics, more likely to use contentious forms of political action, and support left-wing political parties that promote minority interests. Key explanations of differences between ethnic minorities and majorities in Western democracies focus on the importance of individual and group resources as well as political empowerment via representation in policymaking institutions, usually enabled by higher shares of minority populations within electoral districts.
Open Access
Ultrasonic surface profile determination by spatial voting
(IEEE, 2001) Barshan, Billur
A novel spatial voting scheme is described for surface profile determination based on multiple ultrasonic range measurements. Spatial voting relies on the number of votes accumulated in each pixel of the ultrasonic arc map but ignores neighboring relationships. This approach is extremely robust, flexible, and straightforward. It can deal with arbitrary numbers and configurations of sensors as well as synthetic arrays, with the intrinsic ability to suppress spurious readings, crosstalk, and higher-order reflections, and process multiple reflections informatively. The performance of the method is investigated on various examples involving both simulated and experimental data. The effect of varying the surface roughness is also considered.
Open Access
Voting features based classifier with feature construction and its application to predicting financial distress
(Pergamon Press, 2010) Güvenir, H. A.; Çakır, M.
Voting features based classifiers, shortly VFC, have been shown to perform well on most real-world data sets. They are robust to irrelevant features and missing feature values. In this paper, we introduce an extension to VFC, called voting features based classifier with feature construction, VFCC for short, and show its application to the problem of predicting if a bank will encounter financial distress, by analyzing current financial statements. The previously developed VFC learn a set of rules that contain a single condition based on a single feature in their antecedent. The VFCC algorithm proposed in this work, on the other hand, constructs rules whose antecedents may contain conjuncts based on several features. Experimental results on recent financial ratios of banks in Turkey show that the VFCC algorithm achieves better accuracy than other well-known rule learning classification algorithms. © 2009 Elsevier Ltd. All rights reserved.