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We present a new framework and method for solving Multiple Instance Learning (MIL) problems. As a variation on supervised learning, MIL addresses the problem of classifying a bag of instances. If at least one of the instances in a bag is positive the bag is labeled positive, otherwise it is negative. We use a divide and conquer strategy to identify true positive group of instances in the positive...
In BCI research community, EEG based self-paced brain-computer interfaces (SBCI) have been widely researched in the past several years. SBCI systems allow individuals to control outside device using EEG signals at their own pace. But the performance of current SBCI technology is not suitable for most applications due to the difficult in detection of the non-periodic intentionally brain state changing...
Spino Cerebellar Ataxia type 2 is an autosomal dominant cerebellar hereditary ataxia with the highest prevalence in Cuba. Typical symptoms in patients of SCA2 ataxia include modifications in latency, peak velocity, and deviation in visual saccadic movements. After applying some electro-oculography based tests to both healthy and SCA2 afflicted individuals, differences in saccade morphology were found,...
In the recent years human brain segmentation in three-dimensional magnetic resonance imaging (MRI) has gained a lot of importance in the field of biomedical image processing since it is the main stage for the automatic brain disease diagnosis. In this paper, we propose an image segmentation scheme to segment 3D brain tumor from MRI images through the clustering process. The clustering is achieved...
Inspite of the huge amounts of image data on the web, mining image data from the web is paid less attention than mining text data, since treating the semantics of images is much more difficult. This paper introduces a new system to mine visual knowledge on the web that aims to build a Domain Oriented Image Directory by using the Earth Mover's Distance and Color signatures. Instead of using a flat...
A set of distributed continual range query requests, each defining a geographical region of interest, needs to be periodically reevaluated to provide up to date answers. Processing these continual queries efficiently and incrementally becomes important for location based services and applications. In this paper, we propose an efficient incremental method for continuous range query characterized by...
This paper discusses the application of two unsupervised methods in classifying type of soils. Soils that are suitable for agricultural activities can be classified into four classes which are hill soil, organic soil, alteration soil and alluvium soil. In addition, no specific support system is able to classify the type of soil and retrieve the information for location and suitable plants for local...
In this paper we propose a rough classification modeling algorithm based on Ant Colony Optimization (ACO) reduction. We used ACO to compute the rough set reduct and later a modified rules generation method is employed to generate the classification rules. The rules generation algorithm used is the simplification of the Default Rules Generation Framework (DRGF) in order to fit with the ACO reduct....
This paper proposes a new feature-selection strategy by integrating the Rough Set Theory (RST) and Particle Swarm Optimisation (PSO) algorithms to generate a set of discriminatory features for the classification problem. The proposed method is seen as a marriage between filter and wrapper approaches in which the RST is used to pre-reduce the feature set before optimisation by PSO, a meta-heuristic...
Individual protection, physically or mentally, is very important for someone living in a risk environment. Insurance is one of the individual protections due to accident, blaze, critical diseases or death. Insurance company plays a critical role in providing competitive product insurance that covers flexible features depend on customer requirements. In order to compete with other competitors and fulfill...
Paper deals with the problem of designing efficient classifiers for a special case of incremental concept drift. We focus on its classification based on the multiple classifier system. For the problem under consideration we propose four simple methods of combining classification and evaluate them via computer experiments.
Combining pattern recognition is the promising direction in designing an effective classifier systems. There are several approaches of collective decision-making, among them voting methods, where the decision is a combination of individual classifiers' outputs are quite popular. This article focuses on the problem of fuser design which uses continuous outputs of individual classifiers to make a decision...
MicroRNAs (miRNAs) are small Ribonucleic Acid (RNA) molecules ~18-22 nucleotides (nt) in length that regulates gene expression in animals, plants and viruses. Due to its small size and occurrence in different development stages of organisms, the experimental identification of miRNAs becomes difficult, and computational approaches are being developed in order to precede and guide biological experiments...
In this paper, we present an approach to automatically extract and classify opinions in texts. We propose a similarity measurement calculating semantically distances between a word and predefined subgroups of seed words. We have evaluated our algorithm on the semantic evaluation company “SemEval 2007” corpus, and we obtained the best value of Precision and F1 62% and 61%. As an improvement of 20 %...
Linear regression and classification techniques are very common in statistical data analysis but they are often able to extract from data only linear models, which can be a limitation in real data context. Aim of this study is to build an innovative procedure to overcome this defect. Initially, a multiple linear regression analysis using the best-subset algorithm was performed to determine the variables...
One of the major issues concerning the Artificial Neural Networks (ANNs) design is a proper adjustment of the weights of the network. There have been a number of studies comparing the performance of evolutionary and gradient based ANNs learning. But the results of the studies, sometime conflicting to each other although the same and standard dataset development had been used. Motivated by this finding,...
A novel solution is proposed to an important problem of learning real querying preferences and intentions from users who need to retrieve interesting information from a database but are not in a position to specify their information needs and/or intentions using a query language due to lack of knowledge and/or experience. A solution is proposed that is based on the presentation to the user of consecutive...
Autonomous steering control is the principal task in the development of an intelligent transportation system. This research paper proposes a novel approach for vision based intelligent control of unmanned vehicles. The paper addresses the problems of accurate and efficient intelligent vehicle control by incorporating a well known evolutionary algorithm cAnt-Miner. The uniqueness of the proposed algorithm...
Most existing research in the area of emotions recognition has focused on short segments or utterances of speech. In this paper we propose a machine learning system for classifying the overall sentiment of long conversations as being Positive or Negative. Our system has three main phases, first it divides a call into short segments, second it applies machine learning to recognize the emotion for each...
In some machine learning applications using soft labels is more useful and informative than crisp labels. Soft labels indicate the degree of membership of the training data to the given classes. Often only a small number of labeled data is available while unlabeled data is abundant. Therefore, it is important to make use of unlabeled data. In this paper we propose an approach for Fuzzy-Input Fuzzy-Output...
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