Language- and Subtask-Dependent Feature Selection and Classifier Parameter Tuning for Author Profiling
نویسندگان
چکیده
We present the CIC’s approach to the Author Profiling (AP) task at PAN 2017. This year task consists of two subtasks: gender and language variety identification in English, Spanish, Portuguese, and Arabic. We use typed and untyped character n-grams, word n-grams, and non-textual features (domain names). We experimented with various feature representations (binary, raw frequency, normalized frequency, log-entropy weighting, tf-idf), machine-learning algorithms (liblinear and libSVM implementations of Support Vector Machines (SVM), multinomial naive Bayes, ensemble classifier, meta-classifiers), and frequency threshold values. We adjusted system configurations for each of the languages and subtasks.
منابع مشابه
Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets
Objective(s): This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. Materials and Methods: To evaluate effectiveness of proposed feature selection method, we ...
متن کاملA New Hybrid Framework for Filter based Feature Selection using Information Gain and Symmetric Uncertainty (TECHNICAL NOTE)
Feature selection is a pre-processing technique used for eliminating the irrelevant and redundant features which results in enhancing the performance of the classifiers. When a dataset contains more irrelevant and redundant features, it fails to increase the accuracy and also reduces the performance of the classifiers. To avoid them, this paper presents a new hybrid feature selection method usi...
متن کاملGene Identification from Microarray Data for Diagnosis of Acute Myeloid and Lymphoblastic Leukemia Using a Sparse Gene Selection Method
Background: Microarray experiments can simultaneously determine the expression of thousands of genes. Identification of potential genes from microarray data for diagnosis of cancer is important. This study aimed to identify genes for the diagnosis of acute myeloid and lymphoblastic leukemia using a sparse feature selection method. Materials and Methods: In this descriptive study, the expressio...
متن کاملA New Approach for Text Documents Classification with Invasive Weed Optimization and Naive Bayes Classifier
With the fast increase of the documents, using Text Document Classification (TDC) methods has become a crucial matter. This paper presented a hybrid model of Invasive Weed Optimization (IWO) and Naive Bayes (NB) classifier (IWO-NB) for Feature Selection (FS) in order to reduce the big size of features space in TDC. TDC includes different actions such as text processing, feature extraction, form...
متن کاملFeature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine
Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods. In filter methods, features subsets are selected due to some measu...
متن کامل