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Abstract: Text classification has received more and more attention due to the enormous growth of digital content available on-line. This paper investigates the design of two-class text classifiers using positive and unlabeled data only. The specialty of this problem is that there is no labeled negative example for learning, which makes traditional text classification techniques inapplicable. In this paper, a novel semi-supervised classification algorithm based on tolerance rough set and ensemble learning is proposed. Tolerance rough set theory is used to approximate concepts existed in documents and extract an initial set of negative example. Then, SVM, Rocchio and Naive Bayes algorithms are used as base classifiers to construct an ensemble classifier, which runs iteratively and exploits margins between positive and negative data to progressively improve the approximation of negative data. Thus, the class boundary eventually converges to the true boundary of the positive class in the feature space. An experimental evaluation of different methods is carried out on two common text corpora, i.e., the Reuters-21578 collection and the WebKB collection. The experimental results indicate that the proposed method achieves significant performance improvement. [ABSTRACT FROM AUTHOR]

Abstract: Several algorithms exist for testing linear separability. The choice of a particular testing algorithm has effects on the performance of constructive neural network algorithms that are based on the transformation of a nonlinear separability classification problem into a linearly separable one. This paper presents an empirical study of these effects in terms of the topology size, the convergence time, and generalisation level of the neural networks. Six different methods for testing linear separability were used in this study. Four out of the six methods are exact methods and the remaining two are approximative ones. A total of nine machine learning benchmarks were used for this study. [ABSTRACT FROM AUTHOR]

Abstract: The inclusion of irrelevant, redundant, and inconsistent features in the data-mining model results in poor predictions and high computational overhead. This paper proposes a novel information theoretic-based interact (IT-IN) algorithm, which concerns the relevance, redundancy, and consistency of the features. The proposed IT-IN algorithm is compared with existing Interact, FCBF, Relief and CFS feature selection algorithms. To evaluate the classification accuracy of IT-IN and remaining four feature selection algorithms, Naïve Bayes, SVM, and ELM classifier are used for ten UCI repository datasets. The proposed IT-IN performs better than existing above algorithms in terms of number of features. The specially designed hash function is used to speed up the IT-IN algorithms and provides minimum computation time than the Interact algorithms. The result clearly reveals that the proposed feature selection algorithm improves the classification accuracy for ELM, Naïve Bayes, and SVM classifiers. The performance of proposed IT-IN with ELM classifier is superior to other classifiers. [ABSTRACT FROM AUTHOR]

Abstract: Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM) introduced by . The main drawback of these newer techniques is their lack of interpretability. In other words, it is difficult for the human analyst to understand the knowledge learnt by these models during training. The most popular way to overcome this difficulty is to extract if–then rules from SVM and SVR. Rules provide explanation capability to these models and improve the comprehensibility of the system. Over the last decade, different algorithms for extracting rules from SVM have been developed. However rule extraction from SVR is not widely available yet. In this paper a novel hybrid approach for extracting rules from SVR is presented. The proposed hybrid rule extraction procedure has two phases: (1) Obtain the reduced training set in the form of support vectors using SVR (2) Train the machine leaning techniques (with explanation capability) using the reduced training set. Machine learning techniques viz., Classification And Regression Tree (CART), Adaptive Network based Fuzzy Inference System (ANFIS) and Dynamic Evolving Fuzzy Inference System (DENFIS) are used in the phase 2. The proposed hybrid rule extraction procedure is compared to stand-alone CART, ANFIS and DENFIS. Extensive experiments are conducted on five benchmark data sets viz. Auto MPG, Body Fat, Boston Housing, Forest Fires and Pollution, to demonstrate the effectiveness of the proposed approach in generating accurate regression rules. The efficiency of these techniques is measured using Root Mean Squared Error (RMSE). From the results obtained, it is concluded that when the support vectors with the corresponding predicted target values are used, the SVR based hybrids outperform the stand-alone intelligent techniques and also the case when the support vectors with the corresponding actual target values are used. [Copyright &y& Elsevier]

Abstract: Transductive support vector machine (TSVM) is a well-known algorithm that realizes transductive learning in the field of support vector classification. This paper constructs a bi-fuzzy progressive transductive support vector machine (BFPTSVM) algorithm by combining the proposed notation of bi-fuzzy memberships for the temporary labeled sample appeared in progressive learning process and the sample-pruning strategy, which decreases the computation complexity and store memory of algorithm. Simulation experiments show that the BFPTSVM algorithm derives better classification performance and converges rapidly with better stability compared to the other learning algorithms. [Copyright &y& Elsevier]

Abstract: In this paper we formulate a least squares version of the recently proposed twin support vector machine (TSVM) for binary classification. This formulation leads to extremely simple and fast algorithm for generating binary classifiers based on two non-parallel hyperplanes. Here we attempt to solve two modified primal problems of TSVM, instead of two dual problems usually solved. We show that the solution of the two modified primal problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in TSVM. Classification using nonlinear kernel also leads to systems of linear equations. Our experiments on publicly available datasets indicate that the proposed least squares TSVM has comparable classification accuracy to that of TSVM but with considerably lesser computational time. Since linear least squares TSVM can easily handle large datasets, we further went on to investigate its efficiency for text categorization applications. Computational results demonstrate the effectiveness of the proposed method over linear proximal SVM on all the text corpuses considered. [Copyright &y& Elsevier]

In this paper, we propose a novel approach, termed as regularized least squares fuzzy support vector regression, to handle financial time series forecasting. Two key problems in financial time series forecasting are noise and non-stationarity. Here, we assign a higher membership value to data samples that contain more relevant information, where relevance is related to recency in time. The approach requires only a single matrix inversion. For the linear case, the matrix order depends only on the dimension in which the data samples lie, and is independent of the number of samples. The efficacy of the proposed algorithm is demonstrated on financial datasets available in the public domain. [Copyright &y& Elsevier]

Abstract: Support vector machines (SVM) are widely applied to various classification problems. However, most SVM need lengthy computation time when faced with a large and complicated dataset. This research develops a clustering algorithm for efficient learning. The method mainly categorizes data into clusters, and finds critical data in clusters as a substitute for the original data to reduce the computational complexity. The computational experiments presented in this paper show that the clustering algorithm significantly advances SVM learning efficiency. [Copyright &y& Elsevier]

• Artificial immune network (AIN) for optimizing machine learning algorithms. • Convergence analysis of the proposed optimization algorithm is presented. • Successful experimentation on many benchmark machines learning datasets. • Achieved classification performance boost of 2% – 9%. Artificial Immune Networks (AIN) is a population-based evolutionary algorithm that is inspired by theoretical immunology. It applies ideas and metaphors from the biological immune system to solve multi-disciplinary problems. This paper presents a novel application of the AIN for optimizing shallow machine learning (ML) classification algorithms. AIN accomplishes this task by searching the best hyper-parameter set for a specific classification algorithm (also termed model selection), which minimizes training error and enhances the generalization capability of the algorithm. We present a convergence analysis of the proposed algorithm and employ it in conjunction with selected, well-known ML classifiers, namely, an extreme learning machine (ELM), a support vector machine (SVM) and an echo state network (ESN). The performance is evaluated in terms of classification accuracy and learning time, using a range of benchmark datasets, and compared against grid search as well as evolutionary strategy (ES)-based optimization techniques. An empirical study with different datasets demonstrates improved classification accuracy of SVM, from 2% to 5%, for ESN from 3% to 6%, whereas in the case of ELM from 3% to 9%. Comparative simulation results demonstrate the potential of AIN as an alternative optimizer for shallow ML algorithms. [ABSTRACT FROM AUTHOR]

• The proposed hybrid method performs over-sampling and under-sampling effectively. • The sampling process is done quickly and efficiently by clustering. • The roulette wheel selects the most suitable samples through probabilities. • The proposed method does not need to set any parameter. • The proposed method reaches to IR =1 for all imbalanced data sets. The problem of imbalanced data set classification is prevalent in the studies of machine learning and data mining. In these kinds of data sets, the number of samples in classes is unequal so that one class has a lot more samples (the majority or negative class) than the other (the minority or positive class). The classical classifiers are ineffective in these conditions because they are biased toward the majority class and ignore the minority class, which is more important. Preprocessing the data distribution before training the classifier is one of the most effective methods to resolve this problem. These methods, balance the data distribution by decreasing the majority class size (under-sampling methods) or increasing the minority class size (over-sampling methods) or combining both of them (hybrid methods). In this paper, we propose an effective and simple hybrid approach based on the density concept and clustering, which is called Clustering and Density-Based Hybrid (CDBH). First, the minority class samples are clustered by the well-known k -means algorithm and their densities in each cluster are obtained. Then, the denser minority samples are selected with more likely to generate the new minority samples. To decrease the majority class size, the k -means algorithm is applied again on the majority class samples to cluster them and compute their densities, like the previous stage. Finally, the denser majority samples will have more chance to choose from the training set, and other samples are removed to balance the data samples distribution between classes. In the experiments, the Support Vector Machine (SVM) classifier is used as the classifier, and F -measure and AUC criteria are employed for evaluation. Also, preprocessing methods are compared in terms of the complexity of the classification model and the over-sampling rate. The results of comparing CDBH and other state of the art methods over 44 imbalanced data sets show the superiority of the proposed CDBH method based on the F -measure criterion. [ABSTRACT FROM AUTHOR]