Tumor classification based on gene expression levels is important for tumor diagnosis. Since tumor data in gene expression contain thousands of attributes, attribute selection for tumor data in gene expression becomes a key point for tumor classification. Inspired by the concept of gain ratio in decision tree theory, an attribute selection method based on fuzzy gain ratio under the framework of...

Tackling missing data is one of the fundamental data pre-processing steps. Data analysis and pattern extraction are affected due to the underlying differences between instances with and without missing data. This is a particular problem with ordinal data, where for example a sample of a population may have all failed to answer a specific question in a questionnaire. The existing methods such as...

Fairness across different demographic groups is an essential criterion for face-related tasks, Face Attribute Classification (FAC) being a prominent example. Simultaneously, federated Learning (FL) gaining traction as scalable paradigm distributed training. In FL, client models trained on private datasets get aggregated by central aggregator. Existing FL approaches require data homogeneity to e...

Classification using naive bayes algorithm for air quality dataset has an accuracy rate of 39.97%. This result is considered not good and by all existing data attributes. By doing pre-processing, namely feature selection the gain ratio algorithm, Naive Bayes increases to 61.76%. proves that can improve performance classification. dataset. While Water Quality 93.18%. These results are 95.73%. Ba...

Text Classification tasks are becoming increasingly popular in the field of Information Access. Being approached as Machine Learning problems, the definition of suitable attributes for each task is approached in an ad-hoc way. We believe that a more principled framework is required, and we present initial insights on attribute engineering for Text Classification, along with a software library t...

In the domain of object recognition and image classification, a recent trend is to use image properties or attributes to represent the images. Most of the proposed models in the past require that the number of attributes and attribute semantics be specified in advance. In this paper, we propose a generative model for image attributes that combine attribute-based vision models and feature-based ...

Every day, we are observing that a wide-ranging amount of data and information is being stored every moment. Mostly the information is dynamic and transactional, and each time the data is being updated from time to time. Getting knowledge from this kind of huge and dynamic data is really becoming tough task with respect to time and efficiency. Fortunately we are served with the intelligent proc...

It has been well-recognized that the accent has a great impact on the ASR of Chinese Mandarin, therefore, how to improve the performance on the accented speech has become a critical issue in this field. The attribute feature has been proven effective on modelling accented speech, resulting in a significantly improved performance in accent recognition. In this paper, we propose an attribute-base...

The presented Softmax Regression classifier is a generalization of logistic regression. It is used for multi-class classification, where classes are mutually exclusive. Implemented in a classification framework, it provides a flexible approach to customize a classification process. Traditional classification is focused with classifiers that can only be applied on the same dataset. The Softmax R...