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Speech feature learning is very important for the design of classification algorithm of Parkinson's disease (PD). Existing speech feature learning method for classification of PD just pays attention to the speech feature. This paper proposed a novel hybrid feature learning algorithm which puts the features of all the speech segments of each subject together, thereby obtaining new and high efficient...
Click-through rate estimation, the core task of programmatic display advertising, is associated with typical big data problems. Online algorithms for generalized linear models, such as Logistic Regression, are the most widely used data mining techniques for learning at such a massive scale. Since these models are unable to capture the underlying nonlinear data patterns, conjunction features are often...
One of the main tasks of machine learning and data mining is feature selection. Depending on the task different methods applied to find optimal balance between speed and feature selection quality. MeLiF algorithm effectively solves feature selection problem by building ensemble of feature ranking filters. It reduces filters aggregation problem to linear form optimization problem and works as a wrapper,...
Feature selection is a very important technique in machine learning and pattern classification. Feature selection studies using batch learning methods are inefficient when handling big data in real world, especially when data arrives sequentially. Online Feature Selection is a new paradigm which is more efficient than batch feature selection methods but it still very challenging in large-scale ultra-high...
Credit scoring prediction is a focus of banking sector to identify trickery customers and to reduce illegal activities. The usage of ensemble classifiers in machine learning plays a vital role in prediction problems. The aim of this study is to analyze the accuracy of the ensemble methods in classifying the customers as good risk group or bad risk group. In this paper experiments are conducted using...
This study is carried out on application of different machine learning techniques for prediction of features using bioinformatics data such as that of cancer. The prediction process is undertaken based on feature extraction and then on feature selection process. After selecting the relevant features, machine learning techniques like Support vector machine (SVM), Extreme learning machine (ELM) are...
The disease Leukemia are continuously increasing among the people. The cause of leukemia is unknown but several factors, however are associated with the development of leukemia that are exposure to ionising radiation, exposure to benzene in rubber industry workers, cytotoxic drug particularly alkylating agent exposure, genetic disorder like down syndrome and immunological deficiency states. There...
The k Nearest Neighbour (kNN) method is one of the most popular algorithm in clustering and data classification. The kNN algorithm founds to be performed very efficient in the experiments on different dataset. In this paper, we focus on the classification problem. The algorithm is experienced over Leukemia dataset. Initially three feature selection algorithm Consistency Based Feature Selection (CBFS),...
Given a collection of algorithms, the Algorithm Selection (AS) problem consists in identifying which of them is the best one for solving a given problem. The selection depends on a set of numerical features that characterize the problem to solve. In this paper we show the impact of feature selection techniques on the performance of the SUNNY algorithm selector, taking as reference the benchmarks of...
We study a new problem of learning from doubly-streaming data where both data volume and feature space increase over time. We refer to the problem as mining trapezoidal data streams. The problem is challenging because both data volume and feature space are increasing, to which existing online learning, online feature selection and streaming feature selection algorithms are inapplicable. We propose...
Blind measurement of visual quality is of fundamental importance in numerous image and video processing applications. Most of the no-reference Image Quality Assessment (NR IQA) methods are distortion-specific and their application domain is limited. Also, almost all distortion-generic NR IQA are computationally complex, making their applicability in real time applications very limited. In this paper...
This paper presents a domain adaptive action recognition approach, which utilizes labeled training videos taken under one environment (source domain) to train an action classifier for the videos taken under another environment (target domain), so that the cost for preparing training data can be greatly alleviated. Our proposed approach jointly utilizes self-training and feature selecting to gradually...
In microarray analysis, the selection of informative gene is an essential issue for tissue classification and successful treatment because of its ability to improve the accuracy and decrease computational complexity. However, the gene subsets selected by the same method often vary significantly with some variations of the samples in the same data set. Thus, the stability of the selected genes is an...
In this paper, we present a novel approach towards combining various machine learners. Our novel approach shows an increase in the accuracy for solving the classification problems in machine learning. We first present a technique of combining learners and also show its implementation using Python programming and then show its comparison with other learners. Later we discuss feature space design and...
Instance-Specific Algorithm Configuration (ISAC) is a novel general technique for automatically generating and tuning algorithm portfolios. The approach has been very successful in practice, but up to now it has been committed to using all the features it was provided. However, traditional feature filtering techniques are not applicable, requiring multiple computationally expensive tuning steps during...
Support Vector Machines are the state-of-the-art tools in data mining. However, their strength are also their main weakness, as the generated nonlinear models are typically regarded as incomprehensible black-box models. Therefore, opening the black-boxor making SVMs explainable became more important and necessary in areas such as medical diagnosis and credit evaluation. Rule extraction from SVMs,...
Feature selection is an important data preprocessing step in pattern recognition. Recently, a wrapper-type semi-supervised feature selection method, known as FW-SemiFS, was proposed to overcome the small labeled sample problem of supervised feature selection. FW-SemiFS does not consider the confidence of predicted unlabeled data, but rather evaluates the relevance of features according to their frequency...
A new low complexity seizure prediction algorithm is proposed. The algorithm achieves high sensitivity and low false positive rates in 10 out of 18 epileptic patients from the Freiburg database. Its primary achievement is two orders of magnitude computational complexity reduction. The reduced complexity makes an implantable medical device application realizable. In the subset of ten highly predictable...
We propose a novel algorithm for greedy forward feature selection for regularized least-squares (RLS) regression and classification, also known as the least-squares support vector machine or ridge regression. The algorithm, which we call greedy RLS, starts from the empty feature set, and on each iteration adds the feature whose addition provides the best leave-one-out cross-validation performance...
Feature selection is commonly used in bioinformatics applications, such as gene selection from DNA micro array data. Recently, wrapper methods have been proposed as an improvement over traditionally used filter based feature selection methods. In wrapper methods, the goodness of a feature set is often measured using the cross-validation performance of a machine learning method trained with the features...
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