FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification

This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory consumption, and ability to stretch assumptions and time complexity. Attaining a fast computational model providing fuzzy logic and supervised learning is one of the main challenges in the machine learning. In this research paper, we present FSL-BM algorithm as an efficient solution of supervised learning with fuzzy logic processing using binary meta-feature representation using Hamming Distance and Hash function to relax assumptions. While many studies focused on reducing time complexity and increasing accuracy during the last decade, the novel contribution of this proposed solution comes through integration of Hamming Distance, Hash function, binary meta-features, binary classification to provide real time supervised method. Hash Tables (HT) component gives a fast access to existing indices; and therefore, the generation of new indices in a constant time complexity, which supersedes existing fuzzy supervised algorithms with better or comparable results. To summarize, the main contribution of this technique for real-time Fuzzy Supervised Learning is to represent hypothesis through binary input as meta-feature space and creating the Fuzzy Supervised Hash table to train and validate model.Comment: FICC201

Combining Labelled and Unlabelled Data in the Design of Pattern Classification Systems

There has been much interest in applying techniques that incorporate knowledge from unlabelled data into a supervised learning system but less effort has been made to compare the effectiveness of different approaches on real world problems and to analyse the behaviour of the learning system when using different amount of unlabelled data. In this paper an analysis of the performance of supervised methods enforced by unlabelled data and some semisupervised approaches using different ratios of labelled to unlabelled samples is presented. The experimental results show that when supported by unlabelled samples much less labelled data is generally required to build a classifier without compromising the classification performance. If only a very limited amount of labelled data is available the results show high variability and the performance of the final classifier is more dependant on how reliable the labelled data samples are rather than use of additional unlabelled data. Semi-supervised clustering utilising both labelled and unlabelled data have been shown to offer most significant improvements when natural clusters are present in the considered problem

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

An Unsupervised Based Stochastic Parallel Gradient Descent For Fcm Learning Algorithm With Feature Selection For Big Data

Huge amount of the dataset consists millions of explanation and thousands, hundreds of features, which straightforwardly carry their amount of terabytes level. Selection of these hundreds of features for computer visualization and medical imaging applications problems is solved by using learning algorithm in data mining methods such as clustering, classification and feature selection methods .Among them all of data mining algorithm clustering methods which efficiently group similar features and unsimilar features are grouped as one cluster ,in this paper present a novel unsupervised cluster learning methods for feature selection of big dataset samples. The proposed unsupervised cluster learning methods removing irrelevant and unimportant features through the FCM objective function. The performance of proposed unsupervised FCM learning algorithm is robustly precious via the initial centroid values and fuzzification parameter (m). Therefore, the selection of initial centroid for cluster is very important to improve feature selection results for big dataset samples. To carry out this process, propose a novel Stochastic Parallel Gradient Descent (SPGD) method to select initial centroid of clusters for FCM is automatically to speed up process to group similar features and improve the quality of the cluster. So the proposed clustering method is named as SPFCM clustering, where the fuzzification parameter (m) for cluster is optimized using Hybrid Particle Swarm with Genetic (HPSG) algorithm. The algorithm selects features by calculation of distance value between two feature samples via kernel learning for big dataset samples via unsupervised learning and is especially easy to apply. Experimentation work of the proposed SPFCM and existing clustering methods is experimented in UCI machine learning larger dataset samples, it shows that the proposed SPFCM clustering methods produces higher feature selection results when compare to existing feature selection clustering algorithms , and being computationally extremely well-organized. DOI: 10.17762/ijritcc2321-8169.15072