Pattern recognition systems are widely used in a host of different fields. Due to some reasons such as lack of knowledge about a method based on which the best classifier is detected for any arbitrary problem, and thanks to significant improvement in accuracy, researchers turn to ensemble methods in almost every task of pattern recognition. Classification as a major task in pattern recognition, have been subject to this transition. The classifier ensemble which uses a number of base classifiers is considered as meta-classifier to learn any classification problem in pattern recognition. Although some researchers think they are better than single classifiers, they will not be better if some conditions are not met. The most important condition among them is diversity of base classifiers. Generally in design of multiple classifier systems, the more diverse the results of the classifiers, the more appropriate the aggregated result. It has been shown that the necessary diversity for the ensemble can be achieved by manipulation of dataset features, manipulation of data points in dataset, different sub-samplings of dataset, and usage of different classification algorithms. We also propose a new method of creating this diversity. We use Linear Discriminant Analysis to manipulate the data points in dataset. Although the classifier ensemble produced by proposed method may not always outperform all of its base classifiers, it always possesses the diversity needed for creation of an ensemble, and consequently it always outperforms all of its base classifiers on average.

Fake information, better known as hoaxes, is often found on social media. Currently, social media is not only used to make friends or socialize with friends online, but some use it to spread hate speech and false information. Hoaxes are very dangerous in social life, especially in countries with large populations and ethnically diverse cultures, such as Indonesia. Although there have been many studies on detecting false information, the accuracy and efficiency still need to be improved. To help prevent the spread of these hoaxes, we built a model to identify false information in Indonesian using an ensemble classifier that combines the n-gram method, term frequency-inverse document frequency, and passive-aggressive classifier method. The evaluation process was carried out using 5000 samples from Twitter social media accounts in this study. The testing process is carried out using four schemes by dividing the dataset into training and test data based on the ratios of 90: 10, 80: 20, 70: 30, and 60: 40. The inspection results show that our software can accurately detect hoaxes at 91. 8%. We also found an increase in the accuracy and precision of hoax detection testing using the proposed method compared to several previous studies. The results show that our proposed method can be developed and used in detecting hoaxes in Indonesian on various social media platforms.

An emerging technique to improve classification performance is to build several different classifiers, and then to combine them, known as multiple classifier systems or ensemble classification systems. The design process of an ensemble system generally involves two steps: the collection of an ensemble of classifiers and the design of the combination rule. Researchers in various fields including pattern recognition, machine learning and statistics have examined the use of ensemble systems. Nabavi-Kerizi and Kabir provided a review of ensemble classification, where combining techniques have been mainly considered. However, the trend of recent papers in this active field shows that the ensemble systems have focused on different ways to design the ensemble of classifiers. In this paper, first we aim to establish a framework for different approaches. Based on this architecture, each approach has been introduced in details. Combination methods are then described in brief. At the end, active research areas in the field of ensemble learning are presented.

Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific features. Label-specific features means that each class label is supposed to have its own characteristics and is determined by some specific features that are the most discriminative features for that label. LIFT employs clustering methods to discover the properties of data. More precisely, LIFT divides the training instances into positive and negative clusters for each label which respectively consist of the training examples with and without that label. It then selects representative centroids in the positive and negative instances of each label by k-means clustering and replaces the original features of a sample by the distances to these representatives. Constructing new features, the dimensionality of the new space reduces significantly. However, to construct these new features, the original features are needed. Therefore, the complexity of the process of multi-label classification does not diminish, in practice. In this paper, we make a modification on LIFT to reduce the computational burden of the classifier and improve or at least preserve the performance of it, as well. The experimental results show that the proposed algorithm has obtained these goals, simultaneously.

A stroke occurs in the scenario wherein the blood supply to the brain is blocked, leading to a lack of oxygen to the blood. There is a need for the early diagnosis of the stroke to handle the emergency situations of stroke in an efficient manner. Integration of Artificial Intelligence (AI) in the early diagnosis of stroke provides efficiency and flexibility. Artificial Intelligence (AI), which is a mimic of human intelligence has a wide range of applications from small scale systems to high-end enterprise systems. Artificial Intelligence has emerged as an efficient and accurate decision-making system in healthcare systems. Machine Learning (ML) is a subset of Artificial Intelligence (AI). The incorporation of machine learning techniques in stroke diagnosis systems provides faster and precise decisions. The proposed system aims to develop an early diagnosis of stroke disorder using a homogenous logistic regression ensemble classifier. Logistic regression is a linear algorithm that uses maximum likelihood methodology for predictions and a standard machine learning model for two-class problems. The prediction is improved by accumulating the predictions of two or more logistic regression using a bagging ensemble classifier thereby increasing the accuracy of the stroke diagnosis system. The accumulation of prediction of two or more same models is known as a homogenous ensemble classifier. The results obtained show that the proposed homogenous logistic regression ensemble model has higher accuracy than single logistic regression.

One efficient approach in classification is using a set of individual classifiers and then combining their outputs, usually knows as ensemble classification or multiple classifier system. In this paper, an ensemble classification system based on the random subspace approach is employed for diagnosis of endometriosis, in which individual classifiers of the ensemble system are trained with different feature subsets. Finally, for classifying an unknown test sample, classifiers’ outputs are fused using the majority voting combination rule.