Papers by Sharjeel Imtiaz
Recently, Automated Machine Learning (AutoML) has shown considerable growth and industry applicat... more Recently, Automated Machine Learning (AutoML) has shown considerable growth and industry application in the domain of Machine Learning. AutoML main purpose is to improve the learning task by saving time and effort for tasks like preprocessing, feature engineering, model selection, hyperparameters, and model architecture. Generally, AutoML lacks the ability to produce the generalized features for specific or general tasks like sentiment and sarcasm classification, therefore, to overcome the shortcoming of general feature. We proposed generalized feature extraction algorithms Implicit Incongruity (IIA) and Explicit Incongruity (EIA), the main aim is to observe the newly extracted incongruity feature and integrated into AutoML DeepConcat model at search pipeline. Additionally, proposed model will be evaluate using the preprocessing plan with various levels, where each level represents a single preprocessing task like cleaning, the model performance varies with from level to level. BiLS...
s Traditionally, the classification object yields homogeneous object to separate cluster. Few aut... more s Traditionally, the classification object yields homogeneous object to separate cluster. Few authors investigated clustering based on k-Means to distinguish intrusions based on the particular class. Mostly, k-Means algorithm finds out similarity between the object based on distance vector for smallest dataset. We proposed a new approach Jaccard Discrete (J-DIS) based approach which is combines with k-Means to find most similar measures over features attribute values in a larger dataset. Further, this paper is describing best suitable larger dataset taken from KDD CUP-99 dataset [1].Moreover, the J-DIS k-Means approach can be applied over clinical informatics and wireless clustering based routing protocols.
International Journal of Advanced Computer Science and Applications
The credit scoring aim is to classify the customer credit as defaulter or non-defaulter. The cred... more The credit scoring aim is to classify the customer credit as defaulter or non-defaulter. The credit risk analysis is more effective with further boosting and smoothing of the parameters of models. The objective of this paper is to explore the credit score classification models with an imputation technique and without imputation technique. However, data availability is low in case of without imputation because of missing values depletion from the large dataset. On the other hand, imputation based dataset classification accuracy with linear method of ANN is better than other models. The comparison of models with boosting and smoothing shows that error rate is better metric than area under curve (AUC) ratio. It is concluded that artificial neural network (ANN) is better alternative than decision tree and logistic regression when data availability is high in dataset.
Traditionally, the classification object yields homogeneous object to separate cluster. Few autho... more Traditionally, the classification object yields homogeneous object to separate cluster. Few authors investigated clustering based on k-Means to distinguish intrusions based on the particular class. Mostly, k-Means algorithm finds out similarity between the object based on distance vector for smallest dataset. We proposed a new approach Jaccard Discrete (J-DIS) based approach which is combines with k-Means to find most similar measures over features attribute values in a larger dataset. Further, this paper is describing best suitable larger dataset taken from KDD CUP-99 dataset [1].Moreover, the J-DIS k-Means approach can be applied over clinical informatics and wireless clustering based routing protocols.
2005 International Conference on Information and Communication Technologies, 2005
Abstract Early work for unification of information extraction and data mining is motivational and... more Abstract Early work for unification of information extraction and data mining is motivational and problem stated work. This paper proposes a solution framework for unification using intelligent agents. A Relation manager agent extracted feature with cross feedback approach and also provide a Unified Undirected graphical handle. An RPM agent an approach to minimize loop back proposes pooling and model utilization with common parameter for both text and entity level abstractions.
Early work for unification of information extraction and data mining is motivational and problem ... more Early work for unification of information extraction and data mining is motivational and problem stated work. This paper proposes a solution framework for unification using intelligent agents. A Relation manager agent extracted feature with cross feedback approach and also provide a Unified Undirected graphical handle. An RPM agent an approach to minimize loop back proposes pooling and model utilization with common parameter for both text and entity level abstractions.
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Papers by Sharjeel Imtiaz