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Yayın Calculating the VC-dimension of decision trees(IEEE, 2009) Aslan, Özlem; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim EthemWe propose an exhaustive search algorithm that calculates the VC-dimension of univariate decision trees with binary features. The VC-dimension of the univariate decision tree with binary features depends on (i) the VC-dimension values of the left and right subtrees, (ii) the number of inputs, and (iii) the number of nodes in the tree. From a training set of example trees whose VC-dimensions are calculated by exhaustive search, we fit a general regressor to estimate the VC-dimension of any binary tree. These VC-dimension estimates are then used to get VC-generalization bounds for complexity control using SRM in decision trees, i.e., pruning. Our simulation results shows that SRM-pruning using the estimated VC-dimensions finds trees that are as accurate as those pruned using cross-validation.Yayın Univariate margin tree(Springer, 2010) Yıldız, Olcay TanerIn many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we propose a new decision tree learning algorithm called univariate margin tree, where for each continuous attribute, the best split is found using convex optimization. Our simulation results on 47 datasets show that the novel margin tree classifier performs at least as good as C4.5 and LDT with a similar time complexity. For two class datasets it generates smaller trees than C4.5 and LDT without sacrificing from accuracy, and generates significantly more accurate trees than C4.5 and LDT for multiclass datasets with one-vs-rest methodology.Yayın İlişkisel veri tabanlarında mükerrer kayıtların makine öğrenmesiyle tespiti(Institute of Electrical and Electronics Engineers Inc., 2018-07-05) Bayrak, Ahmet Tuğrul; Yılmaz, Aykut İnan; Yılmaz, Kemal Burak; Düzağaç, Remzi; Yıldız, Olcay TanerVeri miktarının artışına paralel olarak, ilişkisel veri tabanlarında mükerrer kayıtlar da artmaktadır. Artan bu kayıtlar kullanıldıkları rapor veya analizlerde tutarsızlığa sebep olabilmektedir. Bu sorunu en aza indirgemek için yaptığımız çalışmada, kayıtların birbirlerine olan benzerlikleri ve alan uzmanlık bilgisiyle belirlenen ağırlıklar, öznitelik olarak kullanılarak makine öğrenmesi algoritmaları ile mükerrer kayıtların bulunması hedeflenmiştir. Yapılan işlem sonucunda 9301467 satır veride 28412 mükerrer çift tespit edilmiştir. Bulunan bu mükerrer kayıtlar veri kaynağından temizlenerek verinin daha tutarlı hale gelmesi sağlanmaktadır.Yayın ISIKUN at the FinCausal 2020: Linguistically informed machine-learning approach for causality identification in financial documents(Association for Computational Linguistics (ACL), 2020) Özenir, Hüseyin Gökberk; Karadeniz, İlknurThis paper presents our participation to the FinCausal-2020 Shared Task whose ultimate aim is to extract cause-effect relations from a given financial text. Our participation includes two systems for the two sub-tasks of the FinCausal-2020 Shared Task. The first sub-task (Task-1) consists of the binary classification of the given sentences as causal meaningful (1) or causal meaningless (0). Our approach for the Task-1 includes applying linear support vector machines after transforming the input sentences into vector representations using term frequency-inverse document frequency scheme with 3-grams. The second sub-task (Task-2) consists of the identification of the cause-effect relations in the sentences, which are detected as causal meaningful. Our approach for the Task-2 is a CRF-based model which uses linguistically informed features. For the Task-1, the obtained results show that there is a small difference between the proposed approach based on linear support vector machines (F-score 94%), which requires less time compared to the BERT-based baseline (F-score 95%). For the Task-2, although a minor modifications such as the learning algorithm type and the feature representations are made in the conditional random fields based baseline (F-score 52%), we have obtained better results (F-score 60%). The source codes for the both tasks are available online (https://github.com/ozenirgokberk/FinCausal2020.git/).Yayın Cost-conscious comparison of supervised learning algorithms over multiple data sets(Elsevier Sci Ltd, 2012-04) Ulaş, Aydın; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim EthemIn the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi(2)Test, a generalization of our previous work, for ordering multiple learning algorithms on multiple data sets from "best" to "worst" where our goodness measure is composed of a prior cost term additional to generalization error. Our simulations show that Multi2Test generates orderings using pairwise tests on error and different types of cost using time and space complexity of the learning algorithms.Yayın Mapping classifiers and datasets(Pergamon-Elsevier Science Ltd, 2011-04) Yıldız, Olcay TanerGiven the posterior probability estimates of 14 classifiers on 38 datasets, we plot two-dimensional maps of classifiers and datasets using principal component analysis (PCA) and Isomap. The similarity between classifiers indicate correlation (or diversity) between them and can be used in deciding whether to include both in an ensemble. Similarly, datasets which are too similar need not both be used in a general comparison experiment. The results show that (i) most of the datasets (approximately two third) we used are similar to each other, (ii) multilayer perceptrons and k-nearest neighbor variants are more similar to each other than support vector machine and decision tree variants. (iii) the number of classes and the sample size has an effect on similarity.Yayın Feature extraction from discrete attributes(IEEE, 2010) Yıldız, Olcay TanerIn many pattern recognition applications, first decision trees are used due to their simplicity and easily interpretable nature. In this paper, we extract new features by combining k discrete attributes, where for each subset of size k of the attributes, we generate all orderings of values of those attributes exhaustively. We then apply the usual univariate decision tree classifier using these orderings as the new attributes. Our simulation results on 16 datasets from UCI repository [2] show that the novel decision tree classifier performs better than the proper in terms of error rate and tree complexity. The same idea can also be applied to other univariate rule learning algorithms such as C4.5Rules [7] and Ripper [3].Yayın Shallow parsing in Turkish(IEEE, 2017) Topsakal, Ozan; Açıkgöz, Onur; Gürkan, Ali Tunca; Kanburoğlu, Ali Buğra; Ertopçu, Burak; Özenç, Berke; Çam, İlker; Avar, Begüm; Ercan, Gökhan; Yıldız, Olcay TanerIn this study, shallow parsing is applied on Turkish sentences. These sentences are used to train and test the per-formances of various learning algorithms with various features specified for shallow parsing in Turkish.Yayın Aynı oteli temsil eden farklı kayıtlar için akıllı eşleştirme(Institute of Electrical and Electronics Engineers Inc., 2019-09) Bayrak, Ahmet Tuğrul; Özbek, Eyüp Erkan; Kestepe, Sedat; Yıldız, Olcay TanerOtel sayısının her geçen gün arttığı turizm sektöründe, aracı firmaların tüm oteller ile ayrı ayrı çalışma imkanı bulunmadığından, firmalar dünya üzerinde bir çok otelle anlaşması bulunan servis sağlayıcılarıyla beraber çalışmaktadır. Farklı servis sağlayıcılarından alınan otel kayıtlarında tekrarlayan otel verileri olabilmektedir. Tekrarlayan bu kayıtlar aynı bilgilere sahip olabileceği gibi, farklı bilgilere sahip olmasına rağmen aynı oteli temsil edebilmektedir. Otel verilerini tutarlı hale getirmek için aynı oteli temsil eden kayıtlar eşleştirilmelidir. Bu amaçla, otel kayıtları üzerinde çalışılarak, adres zenginleştirmesi ve ön işleme yapılan aday kayıtlar için kategorik ve görsel verilerin benzerliklerinin kullanıldığı makine öğrenmesi algoritmaları uygulanmıştır. Yapılan işlem sonucunda, 132.287 satırlık otel verisinde 14.985 adet otel %99,12 doğruluk oranı ile eşleştirilmiştir.Yayın Creating a syntactically felicitous constituency treebank for Turkish(Institute of Electrical and Electronics Engineers Inc., 2020-10-15) Kara, Neslihan; Marşan, Büşra; Özçelik, Merve; Arıcan, Bilge Nas; Kuzgun, Aslı; Cesur, Neslihan; Aslan, Deniz Baran; Yıldız, Olcay TanerIn this study, Bakay et. al [1] and Yildiz et. al.'s [2] work on Turkish constituency treebanks were developed further. Compared to the previous work, the most prominent feature of this study is the fact that every annotation and refinement process is held manually. In addition, constituency treebank created as a result of this study abides by the syntactic rules and typologic features of Turkish while the trees created by previous studies convey only the translated and simply inverted trees that completely ignore the syntactic properties of Turkish. The methodology followed in this study resulted in a significantly more accurate representation of Turkish language and simpler, relatively flatter trees. The straightforward style of trees in this study reduces the complexity and offers a better training dataset for learning algorithms.
