Muhammad Asim

Accuracy Based Feature Ranking Metric for Multi-Label Text Classification

In many application domains, such as machine learning, scene and video classification, data mining, medical diagnosis and machine vision, instances belong to more than one categories. Feature selection in single label text classification is used to reduce the dimensionality of datasets by filtering out irrelevant and redundant features. The process of dimensionality reduction in multi-label classification is a different scenario because here features may belong to more then one classes. Label and instance space is rapidly increasing by the grandiose of Internet, which is challenging for Multi-Label Classification (MLC). Feature selection is crucial for reduction of data in MLC. Method adaptation and data set transformation are two techniques used to select features in multi label text classification. In this paper, we present dataset transformation technique to reduce the dimensionality of multi-label text data. We used two model transformation approaches: Binary Relevance, and Label Power set for transformation of data from multi-label to single label. The Process of feature selection is done using filter approach which utilizes the data to decide the importance of features without applying learning algorithm. In this paper we used a simple measure (ACC2) for feature selection in multi-label text data. We used problem transformation approach to apply single label feature selection measures on multi-label text data; did the comparison of ACC2 with two other feature selection methods, information gain (IG) and Relief measure. Experimentation is done on three bench mark datasets and their empirical evaluation results are shown. ACC2 is found to perform better than IG and Relief in 80% cases of our experiments.

Safety and Role of Gastrointestinal Endoscopy in the Management of Gastrointestinal Acute GVHD in Children After Hematopoietic Stem Cell Transplantation.

Gastrointestinal (GI) endoscopy and biopsy is a common procedure to confirm the diagnosis of acute graft-versus-host disease (aGVHD) in children after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Its safety and benefits in aGVHD management is unclear. We aimed to review the safety and benefits of GI endoscopy and biopsy for GI-aGVHD management. From January 2000 to December 2009, 450 Children received allo-HSCT at SickKids. Seventy-nine (17.5%) patients underwent GI endoscopy and biopsy for suspicion of GI-aGVHD. GI-aGVHD grading was I (n=5), II (n=39), III (n=23), and IV (n=12). GI biopsy confirmed aGVHD in 49 (62%) patients and results were negative in 30 (38%). Thirty-two (40%) patients started treatment based on clinical criteria before procedure. Twenty-four out of 79 patients had a change in therapy because of biopsy results. Treatment change was significantly more common in patients who had a positive biopsy results compared with those with negative results (24/49 vs. 4/30, P=0.02). Comparing patients who started therapy before the biopsy results (n=32) and the remaining patients (n=47) who were not started on therapy, the biopsy results had more impact in altering/starting therapy in these patients (24/47 vs. 0/32, P<0.00001). For the 32 patients who started therapy before the procedure, the biopsy confirmed aGVHD diagnosis in 20 of them (63%). Only 1 patient (1.25%) had duodenal hematoma and needed prolong GI rest and ultimately recovered. GI endoscopy and biopsy was safe and useful in guiding therapy for GI-aGVHD.

Lexical paraphrasing and pseudo relevance feedback for biomedical document retrieval

Term mismatch is a serious problem effecting the performance of information retrieval systems. The problem is more severe in biomedical domain where lot of term variations, abbreviations and synonyms exist. We present query paraphrasing and various term selection combination techniques to overcome this problem. To perform paraphrasing, we use noun words to generate synonyms from Metathesaurus. The new synthesized paraphrases are ranked using statistical information derived from the corpus and relevant documents are retrieved based on top n selected paraphrases. We compare the results with state-of-the-art pseudo relevance feedback based retrieval techniques. In quest of enhancing the results of pseudo relevance feedback approach, we introduce two term selection combination techniques namely Borda Count and Intersection. Surprisingly, combinational techniques performed worse than single term selection techniques. In pseudo relevance feedback approach best algorithms are IG, Rochio and KLD which are performing 33%, 30% and 20% better than other techniques respectively. However, the performance of paraphrasing technique is 20% better than pseudo relevance feedback approach.

Selection of the most relevant terms based on a max-min ratio metric for text classification

Abstract Text classification automatically assigns text documents to one or more predefined categories based on their content. In text classification, data are characterized by a large number of highly sparse terms and highly skewed categories. Working with all the terms in the data has an adverse impact on the accuracy and efficiency of text classification tasks. A feature selection algorithm helps in selecting the most relevant terms. In this paper, we propose a new feature ranking metric called max-min ratio (MMR). It is a product of max-min ratio of the true positives and false positives and their difference, which allows MMR to select smaller subsets of more relevant terms even in the presence of highly skewed classes. This results in performing text classification with higher accuracy and more efficiency. To investigate the effectiveness of our newly proposed metric, we compare its performance against eight metrics (balanced accuracy measure, information gain, chi-squared, Poisson ratio, Gini index, odds ratio, distinguishing feature selector, and normalized difference measure) on six data sets namely WebACE (WAP, K1a, K1b), Reuters (RE0, RE1), and 20 Newsgroups using the multinomial naive Bayes (MNB) and support vector machines (SVM) classifiers. The statistical significance of MMR has been estimated on 5 different splits of training and test data sets using the one-way analysis of variance (ANOVA) method and a multiple comparisons test based on Tukey–Kramer method. We found that performance of MMR is statistically significant than that of the other 8 metrics in 76.2% cases in terms of macro F1 measure and in 74.4% cases in terms of micro F1 measure.

A survey of ontology learning techniques and applications

Abstract Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniques by highlighting their pros and cons. Moreover, it describes the scope and use of ontology learning in several industries. Finally, the paper discusses challenges of ontology learning along with their corresponding future directions.

Improved biomedical term selection in pseudo relevance feedback

Abstract Biomedical information retrieval systems are becoming popular and complex due to massive amount of ever-growing biomedical literature. Users are unable to construct a precise and accurate query that represents the intended information in a clear manner. Therefore, query is expanded with the terms or features that retrieve more relevant information. Selection of appropriate expansion terms plays key role to improve the performance of retrieval task. We propose document frequency chi-square, a newer version of chi-square in pseudo relevance feedback for term selection. The effects of pre-processing on the performance of information retrieval specifically in biomedical domain are also depicted. On average, the proposed algorithm outperformed state-of-the-art term selection algorithms by 88% at pre-defined test points. Our experiments also conclude that, stemming cause a decrease in overall performance of the pseudo relevance feedback based information retrieval system particularly in biomedical domain. Database URL: http://biodb.sdau.edu.cn/gan/

Experimental study of tribological and mechanical properties of TiN coating on AISI 52100 bearing steel

The surface coating is one of the novel approaches to enhance the performance and durability of the mechanical components by decreasing the wear and friction among two interacting bodies. In this study, tribological and mechanical properties of titanium nitride (TiN) coatings were investigated on the AISI 52100 bearing steel deposited by low-temperature physical vapor deposition system. Surface morphology and elemental composition of the TiN coating were analyzed by scanning electron microscope and energy-dispersive X-ray spectrum, respectively. Substrate surface roughness and coating thickness of TiN were varied for correlative analysis among adhesion, mechanical, and tribological properties. Scratch and tribo tests were performed for evaluating the adhesion and tribological properties, respectively. Samples having the substrate surface roughness (0.2 ± 0.05 µm) and the coating thickness of more than 2.83 µm presented relatively better adhesion, wear resistance, and lower coefficient of friction of the TiN coating.