To estimate the type of fertilizer based on soil minerals using voting classifier. For forecasting accuracy %, a Voting Classifier with sample size 10 and Decision Tree was iterated at various times. A supervised learning algorithm is Tree. It constructs “forest” an array decision trees, typically trained to use “bagging” method. Novel Classification predictive model that learns from several mo...

Associative classification (AC) performs much better than other traditional classifiers. It generates a huge number of class association rules (CARs). Since users are interested in the subset rules, constraints introduced generation CARs. Real-world databases record-based which data is continuously added demands incremental mining. Hence, constraint (CCAR) mined from data. To limit and to remov...

Abstract In this work, we present the Gaussian Class-Conditional Simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that improves over state-of-the-art in terms of classification accuracy and adversarial robustness, with little extra cost network training. The proposed method learns mapping input classes onto target distributions latent space such hyperplane c...

In this paper, we propose a new feature selection method called class dependency based feature selection for dimensionality reduction of the macular disease dataset from pattern electroretinography (PERG) signals. In order to diagnosis of macular disease, we have used class dependency based feature selection as feature selection process, fuzzy weighted pre-processing as weighted process and dec...

ABSTRACT This paper describes our work in NTCIR-9 on RITE Binary-class (BC) subtask and Multi-class (MC) subtask in Simplified Chinese. We use classification method and SVM classifier to identify the textual entailment. We totally use thirteen statistical features as the classification features in our system. The system includes three parts: (1) Preprocessing, (2) Feature Extraction, (3) SVM Cl...

Bayesian Classifiers Learn joint distribution P(C,F) Assign to f the most probable class label argmaxc′∈C P(c′, f̃) This defines a classifier, i.e., a map: (F1× . . .×Fm)→ C Credal Classifiers Learn joint credal set P(C,F) Set of optimal classes (e.g., according to maximality ) {c′ ∈ C |@c′′ ∈ C ,∀P ∈ P : P(c′′|f̃) > P(c′|f̃)} This defines a credal classifier, i.e., (F1× . . .×Fm)→ 2 May return mo...

The contrast is exemplified by the following classification task. Let’s assume that we are given data, X , i.e. the observed variable and we want to determine its class label, Y, the unobserved (target) variable. A generative classifier, such as Naive Bayes, makes use of the joint distribution of X and Y, i.e. P(X ,Y) to perform this inference. While a discriminative classifier, such as a Logis...