Performance Analysis of Machine Learning Approaches in Automatic Classification of Arabic Language

Text classification (TC) is a crucial subject. The number of digital files available on the internet is enormous. The goal of TC is to categorize texts into a series of predetermined groups. The number of studies conducted on the English database is significantly higher than the number of studies conducted on the Arabic database. Therefore, this research analyzes the performance of automatic TC of the Arabic language using Machine Learning (ML) approaches. Further, Single-label Arabic News Articles Datasets (SANAD) are introduced, which contain three different datasets, namely Akhbarona, Khaleej, and Arabiya. Initially, the collected texts are pre-processed in which tokenization and stemming occur. In this research, three kinds of stemming are employed, namely light stemming, Khoja stemming, and no- stemming, to evaluate the effect of the pre-processing technique on Arabic TC performance. Moreover, feature extraction and feature weighting are performed; in feature weighting, the term weighting process is completed by the term frequency- inverse document frequency (tf-idf) method. In addition, this research selects C4.5, Support Vector Machine (SVM), and Naïve Bayes (NB) as a classification algorithm. The results indicated that the SVM and NB methods had attained higher accuracy than the C4.5 method. NB achieved the maximum accuracy with a performance of 99.9%

A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear Manifold Distributed Data Classification

$k$ Nearest Neighbors ($k$NN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is applied to nonlinear manifold distributed data, especially when a very limited amount of labeled samples are available. In this paper, we propose a new graph-based $k$NN algorithm which can effectively handle both Gaussian distributed data and nonlinear manifold distributed data. To achieve this goal, we first propose a constrained Tired Random Walk (TRW) by constructing an $R$-level nearest-neighbor strengthened tree over the graph, and then compute a TRW matrix for similarity measurement purposes. After this, the nearest neighbors are identified according to the TRW matrix and the class label of a query point is determined by the sum of all the TRW weights of its nearest neighbors. To deal with online situations, we also propose a new algorithm to handle sequential samples based a local neighborhood reconstruction. Comparison experiments are conducted on both synthetic data sets and real-world data sets to demonstrate the validity of the proposed new $k$NN algorithm and its improvements to other version of $k$NN algorithms. Given the widespread appearance of manifold structures in real-world problems and the popularity of the traditional $k$NN algorithm, the proposed manifold version $k$NN shows promising potential for classifying manifold-distributed data.Comment: 32 pages, 12 figures, 7 table

Using online linear classifiers to filter spam Emails

The performance of two online linear classifiers - the Perceptron and Littlestone’s Winnow – is explored for two anti-spam filtering benchmark corpora - PU1 and Ling-Spam. We study the performance for varying numbers of features, along with three different feature selection methods: Information Gain (IG), Document Frequency (DF) and Odds Ratio. The size of the training set and the number of training iterations are also investigated for both classifiers. The experimental results show that both the Perceptron and Winnow perform much better when using IG or DF than using Odds Ratio. It is further demonstrated that when using IG or DF, the classifiers are insensitive to the number of features and the number of training iterations, and not greatly sensitive to the size of training set. Winnow is shown to slightly outperform the Perceptron. It is also demonstrated that both of these online classifiers perform much better than a standard Naïve Bayes method. The theoretical and implementation computational complexity of these two classifiers are very low, and they are very easily adaptively updated. They outperform most of the published results, while being significantly easier to train and adapt. The analysis and promising experimental results indicate that the Perceptron and Winnow are two very competitive classifiers for anti-spam filtering

Enhanced ontology-based text classification algorithm for structurally organized documents

Text classification (TC) is an important foundation of information retrieval and text mining. The main task of a TC is to predict the text‟s class according to the type of tag given in advance. Most TC algorithms used terms in representing the document which does not consider the relations among the terms. These algorithms represent documents in a space where every word is assumed to be a dimension. As a result such representations generate high dimensionality which gives a negative effect on the classification performance. The objectives of this thesis are to formulate algorithms for classifying text by creating suitable feature vector and reducing the dimension of data which will enhance the classification accuracy. This research combines the ontology and text representation for classification by developing five algorithms. The first and second algorithms namely Concept Feature Vector (CFV) and Structure Feature Vector (SFV), create feature vector to represent the document. The third algorithm is the Ontology Based Text Classification (OBTC) and is designed to reduce the dimensionality of training sets. The fourth and fifth algorithms, Concept Feature Vector_Text Classification (CFV_TC) and Structure Feature Vector_Text Classification (SFV_TC) classify the document to its related set of classes. These proposed algorithms were tested on five different scientific paper datasets downloaded from different digital libraries and repositories. Experimental obtained from the proposed algorithm, CFV_TC and SFV_TC shown better average results in terms of precision, recall, f-measure and accuracy compared against SVM and RSS approaches. The work in this study contributes to exploring the related document in information retrieval and text mining research by using ontology in TC

Hybrid feature selection based on principal component analysis and grey wolf optimizer algorithm for Arabic news article classification

The rapid growth of electronic documents has resulted from the expansion and development of internet technologies. Text-documents classification is a key task in natural language processing that converts unstructured data into structured form and then extract knowledge from it. This conversion generates a high dimensional data that needs further analusis using data mining techniques like feature extraction, feature selection, and classification to derive meaningful insights from the data. Feature selection is a technique used for reducing dimensionality in order to prune the feature space and, as a result, lowering the computational cost and enhancing classification accuracy. This work presents a hybrid filter-wrapper method based on Principal Component Analysis (PCA) as a filter approach to select an appropriate and informative subset of features and Grey Wolf Optimizer (GWO) as wrapper approach (PCA-GWO) to select further informative features. Logistic Regression (LR) is used as an elevator to test the classification accuracy of candidate feature subsets produced by GWO. Three Arabic datasets, namely Alkhaleej, Akhbarona, and Arabiya, are used to assess the efficiency of the proposed method. The experimental results confirm that the proposed method based on PCA-GWO outperforms the baseline classifiers with/without feature selection and other feature selection approaches in terms of classification accuracy

A Machine Learning Technique for Detection of Social Media Fake News

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Textual Query Based Image Retrieval

As digital cameras becoming popular and mobile phones are increased very fast so that consumers photos are increased. So that retrieving the appropriate image depending on content or text based image retrieval techniques has become very vast. Content-based image retrieval, a technique which uses visual contents to search images from large scale image databases according to users interests, has been an active and fast advancing research area semantic gap between the low-level visual features and the high-level semantic concepts. Real-time textual query-based personal photo retrieval system by leveraging millions of Web images and their associated rich textual descriptions. Then user provides a textual query. Our system generates the inverted file to automatically find the positive Web images that are related to the textual query as well as the negative Web images that are irrelevant to the textual query. For that purpose we use k-Nearest Neighbor (kNN), Decision stumps, and linear SVM, to rank personal photos. For improvement of the photo retrieval performance, we have used two relevance feedback methods via cross-domain learning, which effectively utilize both the Web images and personal images. DOI: 10.17762/ijritcc2321-8169.15032