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Feature extraction is an essential step in pattern classification, which is normally divided into two tasks: transforming the input vector into a feature vector and/or reducing its dimensionality. A well-defined feature extraction algorithm makes the subsequent classification process more effective and efficient. One of the most important feature extraction algorithms is linear discriminant analysis...
With the ever increasing production of data from various heterogeneous sources in modern information societies, the need for scalable data-intensive processing is increasing. MapReduce quickly became the de facto framework for large scale data analysis, due to its simple and abstract programming model and its efficient underlying execution system. However, this simplicity comes with a price: its unidirectional...
This paper presents novel time-frequency (t-f) features based on t-f image descriptors for the automatic detection and classification of epileptic seizure activities in EEG data. Most previous methods were based only on signal-related features derived from the instantaneous frequency and the energies of EEG signals generated from different spectral sub-bands. The proposed features are extracted from...
Class imbalance presents a problem when traditional Classification algorithms are applied .In the previous years there are most important substitution and change has been carried out on data classification. Classification of data becomes difficult because of its unbalanced nature. The problem of imbalance class has developed into significant data mining issue. The class imbalance situation arises...
Over past few decades, statistical and soft-computing techniques have become an emerging research area for machine learning problems. Fuzzy logic with better generalization capability and rapport with reality is being used in classification problems immensely. In this paper a fuzzy rule based classification system is modeled as a combinatorial optimization problem. Thus the optimization power of Genetic...
Data mining approaches have been used in business purposes since its inception; however, at present it is used successfully in new and emerging areas like education systems. Government of Bangladesh emphasizes the need to improve the education system. In this research, we use data mining approaches to predict students' final outcome, i.e., final grade in a particular course by overcoming the problem...
In this paper, an Extreme Learning Machine (ELM) based technique for Multi-label classification problems is proposed and discussed. In multi-label classification, each of the input data samples belongs to one or more than one class labels. The traditional binary and multi-class classification problems are the subset of the multi-label problem with the number of labels corresponding to each sample...
Immense databases may contain critical instances or chunks-a small heap of records or instances which has domain specific information. These chunks of information are useful in future decision making for improving classification accuracy for labeling of critical, unlabeled instances by reducing false positives and false negatives. Classification process may be assessed based on efficiency and effectiveness...
In recent days, researchers are actively analysing the human brain to understand the underlying mechanism of heterogeneous psychiatric conditions. Schizophrenia is a severe neurological disorder which has been characterized by varying symptoms namely hallucinations, delusions and cognitive problems. In this paper, we have investigated the resting state fMRI images of 15 normal controls and 12 Schizophrenia...
Feature selection or variable reduction is a fundamental problem in data mining, refers to the process of identifying the few most important features for application of a learning algorithm. The best subset contains the minimum number of dimensions retaining a suitably high accuracy on classifier in representing the original features. The objective of the proposed approach is to reduce the number...
The area of multi-label classification has rapidly developed in recent years. It has become widely known that the baseline binary relevance approach can easily be outperformed by methods which learn labels together. A number of methods have grown around the label power set approach, which models label combinations together as class values in a multi-class problem. We describe the label-power set-based...
Dealing with high dimensionality when learning from data is a tough task since, for example, similarity and correlation in data cannot be properly captured by the conventional notions of distance. Issues are amplified whenever coping with small sample problems, i.e. When the cardinality of the dataset is remarkably smaller than its dimensionality: in these cases, a reliable estimation of the accuracy...
Larger datasets, with many samples are problematic for solving problems in data mining and machine learning, due to increase in computational times, increased complexity, and bad generalization due to outliers. Further, the accuracy and performance of machine learning and statistical models are still based on tuning of some parameters and optimizing them for generating better predictive models of...
Many real-world networks are featured with dynamic changes, such as new nodes and edges, and modification of the node content. Because changes are continuously introduced to the network in a streaming fashion, we refer to such dynamic networks as streaming networks. In this paper, we propose a new classification method for streaming networks, namely streaming network node classification (SNOC). For...
Databases in clinical scenario have tremendous amount of data regarding patients and clinical history associated. Here, data mining plays vital role in searching for patterns within huge clinical data that could provide useful basis of knowledge for efficient and effective decision-making. Classification mechanism is widely used tool of data mining employed in healthcare applications to facilitate...
Humans interact with each other using different communication modalities including speech, gestures and written documents. In the absence of one modality or presence of a noisy modality, other modalities can benefit precision of systems. HCI systems can also benefit from these multimodal communication models for different machine learning tasks. The provision of multiple modalities is motivated by...
Stream mining has gained popularity in recent years due to the availability of numerous data streams from sources such as social media and sensor networks. Data mining on such continuous streams possess a variety of challenges including concept drift and unbounded stream length. Traditional data mining approaches to these problems have difficulty incorporating relational domain knowledge and feature...
Epilepsy is a common neurological disorder which is difficult to treat because of its unpredictable and recurrent nature. The electroencephalogram (EEG) is a valuable tool for detecting epileptic seizures. With the aim of reducing the input feature dimensionality, a single median based feature called interquartile range (IQR) was used in this paper for the classification of normal and seizure EEG...
Speech recognition systems are either based on parametric approach or non-parametric approach. Parametric based systems such as HMMs have been the dominant technology for speech recognition in the past decade. Despite a lot of advancements and enhancements in the design of these systems: key problems such as long term temporal dependence, etc. Has not yet been solved. Recently due to availability...
Reducing the feature dimensionality can improve the computational efficiency of electrocardiogram (ECG) beats classification system. In the long term ECG classification task, vector quantization has demonstrated its advantage in both dimensionality reduction and accuracy increase, but the existing vector quantization methods are not capable of representing the difference of each waveform among ECG...
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