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

ANALYZING BIG DATA WITH DECISION TREES

ANALYZING BIG DATA WITH DECISION TREE

Local Rule-Based Explanations of Black Box Decision Systems

The recent years have witnessed the rise of accurate but obscure decision systems which hide the logic of their internal decision processes to the users. The lack of explanations for the decisions of black box systems is a key ethical issue, and a limitation to the adoption of machine learning components in socially sensitive and safety-critical contexts. %Therefore, we need explanations that reveals the reasons why a predictor takes a certain decision. In this paper we focus on the problem of black box outcome explanation, i.e., explaining the reasons of the decision taken on a specific instance. We propose LORE, an agnostic method able to provide interpretable and faithful explanations. LORE first leans a local interpretable predictor on a synthetic neighborhood generated by a genetic algorithm. Then it derives from the logic of the local interpretable predictor a meaningful explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance's features that lead to a different outcome. Wide experiments show that LORE outperforms existing methods and baselines both in the quality of explanations and in the accuracy in mimicking the black box

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Massively-Parallel Feature Selection for Big Data

We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size). To tackle the challenges of Big Data FS PFBP partitions the data matrix both in terms of rows (samples, training examples) as well as columns (features). By employing the concepts of $p$-values of conditional independence tests and meta-analysis techniques PFBP manages to rely only on computations local to a partition while minimizing communication costs. Then, it employs powerful and safe (asymptotically sound) heuristics to make early, approximate decisions, such as Early Dropping of features from consideration in subsequent iterations, Early Stopping of consideration of features within the same iteration, or Early Return of the winner in each iteration. PFBP provides asymptotic guarantees of optimality for data distributions faithfully representable by a causal network (Bayesian network or maximal ancestral graph). Our empirical analysis confirms a super-linear speedup of the algorithm with increasing sample size, linear scalability with respect to the number of features and processing cores, while dominating other competitive algorithms in its class