Yazar "Uysal, Alper Kursat" seçeneğine göre listele

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    A new dynamic classifier selection method for text classification
    (Tubitak Scientific & Technological Research Council Turkey, 2024) Terzi, Ismail; Uysal, Alper Kursat
    The primary objective of employing multiple classifier systems (MCS) in pattern recognition is to enhance classification accuracy. Dynamic classifier selection (DCS) and dynamic ensemble selection (DES) are two purposeful forms of multiple classifier systems. While DES involves the selection of a classifier set followed by decision combination, DCS opts for the choice of a single competent classifier, eliminating the necessity for classifier combination. As a consequence, DCS methods exhibit superior efficiency in terms of processing time and memory usage compared to DES methods. Moreover, a substantial performance gap exists between the performance of Oracle and both DES and DCS methods. In this study, we introduce a novel method termed dynamic classifier selection technique-decision quotient (DCS-DQ) for text classification based on dynamic classifier selection. Our experimental investigation encompasses four distinct text datasets, with classification accuracy and macro F1-score serving as the primary evaluation criteria. The proposed DCS-DQ method is subjected to comparison with seven state-of-the-art DCS methods. Based on our empirical findings, the DCS-DQ method outperforms the other seven DCS methods in terms of classification accuracy across the majority of feature sizes. Notably, in the Reuters dataset, the classification accuracy of DCS-DQ surpasses that of other DCS methods for all feature sizes except when the feature size is 100. Similarly, in the Ohsumed dataset, the DCS-DQ method demonstrates significant performance improvement, with an accuracy value of 77.02% for 3000 features compared to the maximum accuracy value of 72.74% achieved by the DCS method MCB. Additionally, the performance of the proposed DCS-DQ method closely aligns with the oracle performance compared to the other methods. In conclusion, our proposed DCS-DQ method holds promise for significantly improving classification accuracy in text classification literature.
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    A new metric for feature selection on short text datasets
    (Wiley, 2022) Cekik, Rasim; Uysal, Alper Kursat
    In recent years, short texts are everywhere, especially in social media networks. Short text classification is an essential task for various applications related to the operations on short text documents. In many cases, using the entire feature set causes the high dimensionality problem in short text data. This problem reason of time-consuming and negatively impacts the performance of classifiers. This study presents an effective feature selection algorithm called XY method, which represents the features on XY line and calculates the distance of a feature to the XY line. Also, a value named lambda is calculated. According to this value, the terms are divided into different regions such as negative, positive, and third to determine their discrimination capability. The novel XY method aims to select as few terms as possible in the negative region. The proposed method is evaluated using four different short text datasets with Macro-F1 success measure. In comparisons with other existing feature selection algorithms such as chi-square, information gain, deviation from Poisson distribution, recently proposed max-min ratio, and distinguishing feature selector demonstrate that the XY method achieves either better or competitive performance in significantly reduced various feature sizes.
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    AUTOMATIC CLASSIFICATION OF EFL LEARNERS' SELF-REPORTED TEXT DOCUMENTS ALONG AN AFFECTIVE CONTINUUM
    (Natl Technical Univ Ukraine Kyiv Polytechnic Inst, Fac Linguistics, 2022) Uysal, Derya; Uysal, Alper Kursat
    This study aims to place EFL learners along an affective continuum via machine learning methods and present a new dataset about affective characteristics of EFL learners. In line with the purposes, written self-reports of 475 students from 5 different faculties in 3 universities in Turkey were collected and manually assigned by the researchers to one of the labels (positive, negative, or neutral). As a result, two combinations of the same dataset (AC-2 and AC-3) including different numbers of classes were used for the assessment of automatic classification approaches. Results revealed that automatic classification confirmed the manual classification to a great extent and machine learning methods could be used to classify EFL students along an affective continuum according to their affective characteristics. Maximum accuracy rate of automatic classification is 90.06% on AC-2 dataset including two classes. Similarly, on AC-3 dataset including three classes, maximum accuracy rate of classification is 71.79%. Last, the top-10 features/words obtained by feature selection methods are highly discriminative in terms of assessing student feelings for EFL learning. It could be stated that there is not an existing study in which feature selection methods and classifiers are used in the literature to automatically classify EFL learners??? feelings.